---
title: "Results"
author: "Yiming Wang"
date: "2023-07-29"
output: 
  html_document:
    toc: true
    toc_float: true
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(dplyr)
library(psych)
library(ltm)
library(ggplot2)
library(ggpubr)
library(afex)
library(mediation)
library(lavaan)
library(knitr)
library(mediation)
library(stargazer)
library(dplyr)



df <- read.csv("cleaned data.csv")
table(df$CT1.BB)
# recode the outcome
# only keep the consistent items
cronbach.alpha(df[,c("CT3.B","CT4.B","CT5.B","CT6.B")]) # 0.806
cronbach.alpha(df[,c("TN1.B", "TN2.B", "TN3.B","TN5.B")]) # 0.728
cronbach.alpha(df[,c("TP1.B", "TP2.B", "TP4.B","TP5.B")]) # 0.719

df$conspiracy_belief <- (df$CT3.B + df$CT4.B + df$CT5.B + df$CT6.B)/4
df$trueneg_belief <- (df$TN1.B + df$TN2.B + df$TN3.B + df$TN5.B)/4
df$truepos_belief <- (df$TP1.B + df$TP2.B + df$TP4.B + df$TP5.B)/4

df$conspiracy_belief_binary <- (df$CT3.BB + df$CT4.BB + df$CT5.BB + df$CT6.BB)
df$trueneg_belief_binary <- (df$TN1.BB + df$TN2.BB + df$TN3.BB + df$TN5.BB)
df$truepos_belief_binary <- (df$TP1.BB + df$TP2.BB + df$TP4.BB + df$TP5.BB)

# rename variables
df$feeling_thermo <- df$THERMO_14
nrow(df)

# Group by treatment (mean of negcog): people trust symbolic dethreat more
df %>% group_by(treatment) %>% 
  summarise(mean=mean(NEGCOG),
            .groups = 'drop')
table(df$NEGCOG_1)


# recode treatment

df$real_threat <- 0
df$real_threat[df$treatment == 2] <- 1
df$real_dethreat <- 0
df$real_dethreat[df$treatment == 3] <- 1
df$symb_threat <- 0
df$symb_threat[df$treatment == 4] <- 1
df$symb_dethreat <- 0
df$symb_dethreat[df$treatment == 5] <- 1

df$threat <- 0
df$threat[df$treatment == 2 | df$treatment == 4] <- 1
df$dethreat <- 0
df$dethreat[df$treatment == 2 | df$treatment == 5] <- 1

# recode perceived threat

df$PER_THREAT_1[df$PER_THREAT_1 == "Enemy"] <- 2
df$PER_THREAT_1[df$PER_THREAT_1 == "Neither friend nor enemy"] <- 1
df$PER_THREAT_1[df$PER_THREAT_1 == "Friend"] <- 0
df$PER_THREAT_1 <- as.numeric(df$PER_THREAT_1)

# recode covariates

df$GENDER[df$GENDER == "Male "] <- 0
df$GENDER[df$GENDER == "Female "] <- 1
df$GENDER[df$GENDER == "Prefer not to say"] <- NA
df$GENDER[df$GENDER == "Prefer to self-describe:"] <- NA
df$GENDER <- as.numeric(df$GENDER)

df$race[df$race == "White"] <- 1
df$race[df$race != 1] <- 0
df$race <- as.numeric(df$race)

```


## load the process() function
```{r warning = FALSE, message = FALSE, results = "asis"}

# PROCESS for R version 4.3.1
# Written by Andrew F. Hayes
# www.afhayes.com
# www.processmacro.org
# Copyright 2012-2022 by Andrew F. Hayes ALL RIGHTS RESERVED
# Documented in http://www.guilford.com/p/hayes3 and supplements
# PROCESS workshop schedule at http://haskayne.ucalgary.ca/CCRAM
#
# Distribution of this code in any form except through processmacro.org 
# is prohibited without the permission of the copyright holder, as is
# distribution after modification.
#
# THIS SOFTWARE IS PROVIDED AS IS, WITHOUT WARRANTY OF ANY KIND
# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT
# IN NO EVENT SHALL THE COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM
# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT
# OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
# SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE
# USE OF THIS SOFTWARE IMPLIES AGREEMENT WITH THESE TERMS
#
# To activate, run this script. It may take a few minutes. 
# This will produce a function called process. The other functions
# it creates cannot be accessed by the user but are used by the master 
# process function. This code was not written to be easy for others to
# understand or to look pretty. It was written to work and make some things
# easier to do than they otherwise would be.

process.bcboot3<-function(databcbt,estmte,xp2,badend,priorlo,priorhi)
{
 databcbt<-as.matrix(sort(databcbt))
 badlo<-0;badhi<-0
 pv<-matrix(as.numeric(databcbt < estmte));
 pv<-sum(pv)/nrow(databcbt);ppv<-pv;
 if (pv > .5){ppv<-(1-pv)}
 y5<-sqrt(-2*log(ppv))
 p0<-(-.322232431088);p1<-(-1);p2<-(-.342242088547);p3<-(-.0204231210245)
 p4<-(-.0000453642210148);q0<-(.0993484626060);q1<-(.588581570495)
 q2<-(.531103462366);q3<-(.103537752850);q4<-(.0038560700634)
 xp<-y5+((((y5*p4+p3)*y5+p2)*y5+p1)*y5+p0)/((((y5*q4+q3)*y5+q2)*y5+q1)*y5+q0)
 if (pv <= .5){xp<-(-xp)}
 cilow<-round(nrow(databcbt)*pnorm(2*xp-xp2))
 cihigh<-trunc(nrow(databcbt)*pnorm(2*xp+xp2))+1
 if (cilow < 1){cilow<-1;booterr<-1;badlo<-1}
 if (cihigh > nrow(databcbt)){cihigh<-nrow(databcbt);booterr<-1;badhi<-1}
 llcit<-databcbt[cilow,1]
 ulcit<-databcbt[cihigh,1]
 if ((badlo==1) & (llcit != priorlo)){priorlo<-llcit;badend<-c(badend,llcit)}
 if ((badhi==1) & (ulcit != priorhi)){priorhi<-ulcit;badend<-c(badend,ulcit)}
 bootse<-sd(databcbt)
 cires<-as.matrix(c(bootse,llcit,ulcit))
 cires<-list(cires,badend,priorlo,priorhi)
 return(cires)
}

process.pboot3<-function(databcbt,lcval,hcval)
{
 databcbt<-as.matrix(sort(databcbt))
 llcit<-databcbt[lcval,1]
 ulcit<-databcbt[hcval,1]
 bootse<-sd(databcbt)
 cires<-as.matrix(c(bootse,llcit,ulcit))
 return(cires)
}

process.outform3<-function(outtodo,outbig,resultm,outformc=0)
{
 if ((ncol(outtodo)==1) & (outformc==0)){outtodo<-t(outtodo)}
 resultm2<-matrix(99999,nrow(outtodo),outbig)
 if (ncol(outtodo) <= outbig)
  {
   resultm2[1:nrow(outtodo),1:ncol(outtodo)]<-outtodo
   resultm<-rbind(resultm,resultm2)
   maxresm<-ncol(resultm)
  }
 if (ncol(outtodo) > outbig)
  {
   resultmt<-matrix(99999,nrow(resultm),ncol(outtodo))
   resultmt[1:nrow(resultm),1:ncol(resultm)]<-resultm
   resultm<-resultmt
   resultm2<-matrix(99999,nrow(outtodo),ncol(resultm))
   resultm2[1:nrow(outtodo),1:ncol(outtodo)]<-outtodo
   resultm<-rbind(resultm,resultm2)
   maxresm<-ncol(resultm)
  }
  outform3res<-list(resultm,maxresm)
  return(outform3res)
}

process.llrtest3<-function(lm,y,x,b,basemod,iterate,converge)
{
 lm<-as.matrix(lm)
 btemphld<-b
 llrdat<-matrix(-999,nrow(x),(nrow(lm)-sum(lm)))
 llrdf<-ncol(x)-ncol(llrdat)
 llrcnt<-0
 for (llri in (1:nrow(lm)))
 {
  if (lm[llri,1]==0){llrcnt<-llrcnt+1;llrdat[,llrcnt]<-x[,llri]}
 }
 LL2<-process.modelest(y,llrdat,2,0,xp2,5,iterate,converge)
 b<-btemphld
 pvchi<-(1-pchisq((LL2-basemod),df=llrdf))
 fresult<-cbind((LL2-basemod),llrdf,pvchi)
 return(fresult)
}

process.describ3<-function(descdatf,type=0,quantle=1)
{
 desctmp<-matrix(-999,(8-(4*type)),ncol(descdatf))
 # mean, sd, min, max, 16th, 50th, 84th, dich toggle
 for (jd in c(1:ncol(descdatf)))
 {
  descdat<-descdatf[,jd]
  #get the mean, sd, minimum, and maximum */
  desctmp[1,jd]<-mean(descdat)
  desctmp[2,jd]<-sd(descdat)
  desctmp[3,jd]<-min(descdat)
  desctmp[4,jd]<-max(descdat)
  if (type==0)
  {
   minwarn<-0;maxwarn<-0
   tmp=as.numeric(descdat==desctmp[3,jd])+as.numeric(descdat==desctmp[4,jd])
   desctmp[8,jd]<-as.numeric(sum(tmp)==length(tmp))
   if (desctmp[3,jd]==desctmp[4,jd]){desctmp[8,jd]<-2}
   descdat<-matrix(sort(descdat))
   decval<-c(.16,.5,.84)
   for (kd in c(1:3))
   {
    low<-trunc(decval[kd]*(length(descdat)+1))
    lowdec<-decval[kd]*(length(descdat)+1)-low
    value<-descdat[low,1]+(descdat[(low+1),1]-descdat[low,1])*lowdec
    desctmp[(4+kd),jd]<-value
   }
   mnotev<-(1)
   modvals<-matrix(desctmp[5:7,],ncol=ncol(descdatf))
   if (quantle != 1)
   {
    desctmp[5,jd]<-desctmp[1,jd]-desctmp[2,jd]
    desctmp[6,jd]<-desctmp[1,jd]
    desctmp[7,jd]<-desctmp[1,jd]+desctmp[2,jd]
    modvals<-matrix(desctmp[5:7,],ncol=ncol(descdatf))
    mnotev<-(2)
    if (modvals[1,1] < desctmp[3,1]){modvals[1,1]<-desctmp[3,1];minwarn<-1}
    if (modvals[3,1] > desctmp[4,1]){modvals[3,1]<-desctmp[4,1];maxwarn<-1}
   }
   if (desctmp[8,jd]==1)
   {modvals<-matrix(c(desctmp[3,1],desctmp[4,1]))
   mnotev<-0;minwarn<-0;maxwarn<-0
   }
   descrtrn<-list(desctmp,modvals,minwarn,maxwarn,mnotev)
  }
 }
 if (type==1)
 {descrtrn<-list(desctmp)}
 return(descrtrn)
}

process.ftest3<-function(lm,bcoef,cv=0,chr=0,brsq=0,skip=0,y,x)
{
 lmat2<-as.matrix(lm)
 y<-as.matrix(y)
 x<-as.matrix(x)
 n<-nrow(y)
 if (skip==0)
 {
  lmat2<-as.matrix(diag(as.numeric(lm)))
  lmat3<-matrix(0,nrow(lmat2),1)
  for (flp in c(1:ncol(lmat2)))
  {
   if (sum(lmat2[,flp])==1)
   {lmat3<-cbind(as.matrix(lmat3),as.matrix(lmat2[,flp]))}
  }
  lmat2<-as.matrix(lmat3[,2:ncol(lmat3)])
 }
 fratio<-(t(t(lmat2)%*%bcoef)%*%solve(t(lmat2)%*%cv%*%lmat2)%*%((t(lmat2)%*%bcoef)))/ncol(lmat2)
 pfr<-(1-pf(fratio,ncol(lmat2),(n-nrow(bcoef))))
 fresult<-matrix(c(fratio,ncol(lmat2),(n-nrow(bcoef)),pfr),ncol=4)
 if (chr==1)
 {
  lmat3<-as.matrix(1-rowSums(lmat2))
  xfm<-matrix(0,n,sum(lmat3))
  flpc<-1
  for (flp in (1:nrow(lmat3)))
  {
   if (lmat3[flp,1]==1){xfm[,flpc]=x[,flp];flpc<-flpc+1}
  }
  bfm<-solve(t(xfm)%*%xfm)%*%t(xfm)%*%y
  resid<-y-(xfm%*%bfm)
  sstotal<-t(y-(sum(y)/n))%*%(y-(sum(y)/n))
  ssresid<-t(resid)%*%resid
  rsqch<-as.numeric(brsq)-((sstotal-ssresid)/sstotal)
  fresult<-matrix(c(rsqch,fresult),ncol=5)
 }
 ftestout<-as.matrix(fresult)
 return(ftestout)
}

#type1 for ols, type2 for logistic LLR, type3 for logistic bootstrapping
process.modelest<-function(y,x,type,full,xp2,hc=5,iterate=100,converge=.00001)
{
 if (type==1)
 {
  invxtx<-solve(t(x)%*%x)
  b<-invxtx%*%t(x)%*%y
  modres<-b
  if (full==1)
  {
   n1<-nrow(x)
   dfres<-(n1-(ncol(x)))
   sstotal<-t(y-(sum(y)/n1))%*%(y-(sum(y)/n1))
   resid=y-x%*%b
   ssresid<-sum(t(resid)%*%resid)       
   r2<-(sstotal-ssresid)/sstotal
   adjr2<-(1-((1-r2)*(n1-1)/(dfres)))
   mse<-ssresid/(n1-ncol(x))
   #HC covariance matrix
   varb<-mse*invxtx
   k3<-ncol(x)
   xhc<-0
   if (hc != 5)
   {
    xhc<-x
    hat<-matrix(xhc[,1])
    for (i3 in c(1:nrow(xhc)))
    {
     xhcm<-matrix(xhc[i3,])
     hat[i3,1]<-t(xhcm)%*%invxtx%*%xhcm
    }     
    if ((hc==0) | (hc==1))
    {
     for (i3 in c(1:k3)){xhc[,i3]<-xhc[,i3]*resid}
    }
    if ((hc==3) | (hc==2))
    {
     for (i3 in c(1:k3))
     {xhc[,i3]<-(resid/(1-hat)^(1/(4-hc)))*xhc[,i3]}
    }
    if (hc==4)
    {
     hcmn<-matrix(4,n1,2);hcmn[,2]<-(n1*hat)/k3
     minr<-apply(hcmn,1,FUN=min)
     for (i3 in c(1:k3))
     {
      xhc[,i3]<-(resid/(1-hat)^(minr/2))*xhc[,i3]
     }
    }
    varb<-(invxtx%*%t(xhc)%*%xhc%*%invxtx)
    if (hc==1){varb<-(n1/(n1-ncol(x)))*varb}
   }
   seb<-sqrt(diag(varb))
   trat<-b/seb
   p<-2*pt(-abs(trat),df=dfres)
   tval<-sqrt(dfres* (exp((dfres-(5/6))*((xp2/(dfres-(2/3)+(.11/dfres)))*(xp2/(dfres-(2/3)+(.11/dfres)))))-1))
   modres<-matrix(c(modres,seb, trat,p,(b-tval*seb),(b+tval*seb)),ncol=6)
   modresl<-t(matrix(c("coeff","hclab","t","p","LLCI","ULCI")))
   lmat<-diag(ncol(x));lmat<-lmat[,2:ncol(lmat)]
   fratio<-(t(t(lmat)%*%b)%*%solve(t(lmat)%*%varb%*%lmat)%*%((t(lmat)%*%b)))/(ncol(x)-1)
   pfr<-1-pf(fratio,(ncol(x)-1),dfres)
   modsum=matrix(c(sqrt(r2),r2,mse,fratio,(ncol(x)-1),dfres,pfr))
   modsuml=matrix(c("R","R-sq","MSE","hcflab","df1","df2", "p"))
   modretrn<-list(modres,modresl,modsum,modsuml,b,varb,tval,resid)
   return(modretrn)
  }
  if (full==0){return(modres)} 
 }
 #for logistic Y model
 if ((type==2) | (type==3))
 {
  xlp<-x;ylp<-as.matrix(y)
  pt2<-matrix((sum(ylp)/nrow(ylp)),nrow(ylp),1)
  if ((type==2)|(type==3)) {LL3<-(ylp*log(pt2)+(1-ylp)*log(1-pt2))}    
  LL3<-(-2*sum(LL3))
  bt1<-matrix(0,ncol(xlp),1);LL1<-0
  pt1<-matrix(0.5,nrow(ylp),1);pt1lp<-pt1
  for (jjj in (1:iterate))
  {
   xlptmp<-t(xlp)
   vecprb<-(pt1lp*(1-pt1lp))
   for (kkk in (1:ncol(xlp))){xlptmp[kkk,]<-xlptmp[kkk,]*t(vecprb)}
   b<-bt1+solve(xlptmp%*%xlp)%*%t(xlp)%*%(ylp-pt1lp)
   if ((type==2) | (type==3))
   {
    xlpb<-xlp%*%b
    xlpbt<-as.numeric(xlpb > -709.7)
    xlpb709<-((1-xlpbt)*(-709.7))
    xlpb<-((xlpb*xlpbt)+xlpb709)
    pt1lp<-1/(1+exp(-(xlpb)))
   }
   itprob<-sum((pt1lp < .00000001) | (pt1lp > .9999999))
   if (itprob > 0)
   {
    for (kkk in (1:nrow(pt1lp)))
    {
     if (pt1lp[kkk,1] > .9999999){pt1lp[kkk,1]<-.9999999}     
     if (pt1lp[kkk,1] < .00000001){pt1lp[kkk,1]<-.00000001}     
    }
    itprob<-0
   }
   if (itprob==0)
   {
    if ((type==2)|(type==3)){LL<-(ylp*log(pt1lp)+(1-ylp)*log(1-pt1lp))}
    LL2<-(-2*sum(LL))
   }
   if (abs(LL1-LL2) < converge)
   {
    if (full==1)
    {
     xlptmp<-t(xlp)
     vecprb<-(pt1lp*(1-pt1lp))   
     for (kkk in (1:ncol(xlp))){xlptmp[kkk,]<-xlptmp[kkk,]*t(vecprb)} 
     varb<-solve(xlptmp%*%xlp)
     seb<-matrix(sqrt(diag(varb)))
    }
    break
   }
   bt1<-b;LL1<-LL2
  }
  modres<-b
  if (jjj > iterate)
  {
   itprob<-2
   if (booting==0){iterrmod<-1}   
   if (booting==1){bootiter==1}   
   if (itprobtg==0)
   {   
    itprobtg<-1;errcode[errs,1]<-47;errs<-errs+1
    if ((booting==0) & (full==1))
    {
     pt1lpc<-(pt1lp*(1-pt1lp))
     vt1<-diag(pt1lpc)
     varb<-solve(t(xlp)%*%vt1%*%xlp)
     seb<-matrix(sqrt(diag(varb)))
    }
   }
  }
  if (full==1)
  {
   trat<-b/seb
   dfres<-nrow(xlp)   
   p<-2*(1-pnorm(abs(trat)))
   modres<-cbind(modres,seb,trat,p) 
   modres<-matrix(c(modres,(b-xp2*seb),(b+xp2*seb)),ncol=6)
   pvchi<-(1-pchisq((LL3-LL2),df=(nrow(modres)-1)))
   mcF<-(LL3-LL2)/LL3
   cox<-1-exp(-(LL3-LL2)/nrow(xlp))
   nagel<-cox/(1-exp(-(LL3)/nrow(xlp)))
   modsum<-matrix(c(LL2,(LL3-LL2),(nrow(modres)-1),pvchi, mcF,cox,nagel))
   modsuml<-matrix(c("-2LL","ModelLL", "df", "p", "McFadden", "CoxSnell", "Nagelkrk"))
   modresl<-t(matrix(c("coeff","se","Z","p","LLCI","ULCI")))
   modretrn<-list(modres,modresl,modsum,modsuml,b,varb,xp2)
   return(modretrn)
  }
  if ((full==0) & (type==2)){return(LL2)} 
  if ((full==0) & (type==3)){return(modres)}
 }
}


process.dummy3<-function(dd)
{
 uq<-unique(dd)
 uq<-matrix(unlist(uq[1]))
 dummy<-matrix(0,nrow(dd),nrow(uq))
 uq<-matrix(sort(uq))
 uq2<-matrix(seq(1:nrow(uq)))
 for (i in c(1:nrow(dd)))
 {for (j in c(1:nrow(uq)))
  {if (dd[i,1]==uq[j,1])
   {dummy[i,uq2[j,1]]<-1}  
  }
 }
return(dummy)
}

process.makdummy<-function(dd,method,custcov=0,custcode=999)
{
 dd<-dd[order(dd[,2]),]
 newrow<-dd[,1]
 dd<-dd[,2]
 dd<-as.data.frame(dd)
 dummy<-process.dummy3(dd)
 uq<-unique(dd);uq<-matrix(unlist(uq[1]))
 criterrd<-0;errcode5<-0;errcode4<-0;errcode6<-0
 nvls<-ncol(dummy)
 nnvls<-colSums(dummy)
 mnvls<-min(nnvls)
 nnvls<-matrix(sort(uq))
 conmat1<-1
 if (mnvls < 2){errcode5<-1;criterrd<-1}
 if (nvls > 9){errcode4<-1;criterrd<-1}
 x<-0;dummat<-0
 if (criterrd==0)
 {
  dumok<-1
  if (method > 0)
  {
   x<-dummy[,2:ncol(dummy)]
   nx<-ncol(x)
   minus1<-matrix(-1,1,ncol(x))
   if (method==4)
   {
    for (k in (1:nrow(dd)))
    {shole<-sum(x[k,])
    if (shole==0)
    {x[k,]<-minus1}}
   }
   if ((method==2) | (method==3) | (method==5))
   {
    for (k in (1:nrow(dd)))
    {shole<-sum(x[k,])
     if (shole > 0)
     {
      for (i in (1:ncol(x)))
      {if (x[k,i]==0){x[k,i]<-1} else{break}
      }
     }
    }
    if (method==3)
    {
     conmat1=c(-8,1,1,1,1,1,1,1,1,0,-7,1,1,1,1,1,1,1,0,0,-6,1,1,1,1,1,1,0,0,0,-5,1,1,1,1,1,0,0,0,0,-4,1,1,1,1,0,0,0,0,0,-3,1,1,1,0,0,0,0,0,0,-2,1,1,0,0,0,0,0,0,0,-1,1)
     conmat1<-t(matrix(conmat1,9,8))
     for (i in (1:8)){conmat1[i,]=conmat1[i,]/(10-i)}
     conmat1<-t(conmat1[(10-nvls):8,(10-nvls):9])
     for (k in (1:nrow(dd)))
     {shole<-sum(x[k,]);x[k,]=conmat1[(shole+1),]}
    }
   }
   if (method==5)
   {
    if (ncol(custcode) != (nvls*(nvls-1)))
    {errcode6<-1}
    if (ncol(custcode)==(nvls*(nvls-1)))
    {
     conmat1<-matrix(0,nvls,(nvls-1));cnt<-1
     for (i in (1:nvls))
     {
      for (k in (1:(nvls-1)))
      {conmat1[i,k]<-custcode[1,cnt];cnt<-cnt+1}
     }
     for (k in (1:nrow(dd))){x[k,]<-conmat1[(sum(x[k,])+1),]}   #check this
    }
   }
   xskip<-1
   dummat<-matrix(0,(nx+1),nx)
   dummat[(2:nrow(dummat)),]<-diag(nx)
   if (method==4){dummat[1,]<-minus1}
   if (method==2)
   {for (i in 2:nrow(dummat))
    {for (j in (1:(i-1))){dummat[i,j]<-1}}
   }
   if (method==3){dummat<-conmat1}
   if ((method==5) & (errcode6==0)){dummat<-conmat1}
   dummat<-cbind(nnvls,dummat)
  } 
  x<-cbind(newrow,x)
  x<-x[order(x[,1]),]
 }
 dummrn<-list(x,dummat,nvls,nnvls,errcode4,errcode5,errcode6)
 return(dummrn)
}



process<-function(data,y="xxxxx",x="xxxxx",m="xxxxx",w="xxxxx",z="xxxxx",cov="xxxxx",
      model=999,converge=.00001,iterate=100,hc=5,jn=0,effsize=0,stand=0,xmtest=0,normal=0,
      mdichok=0,contrast=999,modelbt=0,matrices=0,covmy=0,covcoeff=0,boot=5000,mc=0,
      intprobe=0.1,plot=0,total=0,save=0,mcx=0,mcw=0,mcz=0,moments=0,progress=1,exclude=0,
      bmatrix=-999,wmatrix=-999,zmatrix=-999,wzmatrix=-999,cmatrix=-999,xcatcode=999,
      wcatcode=999,zcatcode=999,wmodval=999,zmodval=999,center=0,conf=95,seed=-999,
      decimals=9.4,maxboot=0,modelres=0,bc=0,outscreen=1,activate=0,describe=0,listmiss=0,
      linsum=-999,xmint=0,xrefval=999,coval=-999,cdeval=-999)
{

 #all this is initiation of variables and matrices
 ranseed<-0;
 if (seed != -999)
 {seed<-trunc(abs(seed));set.seed(seed)}
 wnames<-w;znames<-z;mcerpt<-0;wiscov<-0;ziscov<-0;itprobtg<-0;
 v2tag<-0;maxwwarn<-0;minwwarn<-0;maxzwarn<-0;minzwarn<-0
 toomany<-0;wdich<-0;zdich<-0;wnotev<-0;znotev<-0;singlr<-0
 nxpval<-1;nwpval<-1;nzpval<-1;errs<-1;notes<-1;criterr<-0
 novar<-0;adjust<-0;ncs<-0;serial<-0;sobelok<-0;hasw<-0;ydich<-0;
 hasz<-0;printw<-0;printz<-0;xmint<-as.numeric(xmint==1);
 wmodcust<-0;zmodcust<-0
 booting<-0;bootiter<-0;iterrmod<-0;model<-trunc(model)
 errcode<-matrix(0,100,1);notecode<-matrix(0,100,1)
 iterate<-abs(trunc(iterate));converge=abs(converge);badend<-0;booterr<-0;
 itprobtg<-0;v2tag<-0;maxwwarn<-0;minwwarn<-0;maxzwarn<-0
 minzwarn<-0;toomany<-0;wdich<-0;zdich<-0;wnotev<-0;znotev<-0
 nws<-0;nzs<-0;nms<-0;nys<-0;nxs<-0;maxresm<-9;bc<-as.numeric(bc==1);progress<-as.numeric(progress==1)
 mcxok<-0;mcwok<-0;mczok<-0;xprod<-0;zprod<-0;wprod<-0;modcok<-0;alttotal=0;bc<-as.numeric(bc==1)
 jn<-as.numeric(jn==1);effsize<-as.numeric(effsize==1);maxboots=abs(trunc(maxboot))
 normal<-as.numeric(normal==1);xmtest<-as.numeric(xmtest==1);modelres<-as.numeric(modelres==1)
 stand<-as.numeric(stand==1);outscreen<-as.numeric(outscreen==1);activate<-as.numeric(activate==1)
 xrefvals<-t(matrix(xrefval));xcontcf<-0;xscaling<-1;
 cdeval<-t(matrix(cdeval))
 cuscoval=0
 if (model==74)
  {errcode[errs,1]<-7;errs<-errs+1;criterr<-1}
 if ((xmint==1) & (model != 4))
  {errcode[errs,1]<-63;errs<-errs+1;criterr<-1}
 if ((xmint==1) & (model==4))
  {
   w<-x;model<-74;intprobe<-1
   notecode[notes,1]<-32;
   notes<-notes+1
   if ((effsize==1) | (stand==1))
   {notecode[notes,1]<-34;notes<-notes+1;stand<-0;effsize<-0}
   if (center != 0)
   {center<-0;errcode[errs,1]<-71;errs<-errs+1;criterr<-1}
  }
 describe<-as.numeric(describe==1);listmiss<-as.numeric(listmiss==1);
 if (stand==1) {effsize<-1}
 pstog<-0;sobelok<-0;mdichok=as.numeric(mdichok==1)
 resultm<-matrix(99999,1,maxresm)
 linsum<-t(matrix(linsum))
 nlinsum<-ncol(linsum)
 if (linsum[1,1]==-999){nlinsum<-0}
 deleteme<-matrix(exclude)
 #contrast matrix
 contrast<-matrix(contrast)
 contrast<-t(contrast)
 ncontr<-ncol(contrast)
 if (contrast[1,1]==999) {ncontr<-1;contrast[1,1]<-0}
 if (ncontr==1)
 {
  contrast<-trunc(contrast)
  if ((contrast[1,1] > 2) | (contrast[1,1] < 0))
  {ncontr<-1;contrast[1,1]<-0}
 }
 if (ncontr > 1)
 {
  contvec<-contrast;contrast[1,1]<-3
  if (((model==2) | (model==3)) & (nms==0))
  {
   if (ncontr==4)
   {
   contvec<-t(matrix(contvec,2,2))
   contrast[1,1]<-0;modcok<-1;wcontval<-matrix(contvec[,1]);zcontval<-matrix(contvec[,2])
   }
   if (ncontr !=4)
   {
    notecode[notes,1]<-19;notes<-notes+1
    contrast[1,1]<-0
   }
  }
 }
 contrast<-contrast[1,1]
 if ((xmint==1) & (contrast != 0))
  {contrast<-0;notecode[notes,1]<-37;notes<-notes+1}
 modelbt<-as.numeric(modelbt==1);matrices<-as.numeric(matrices==1)
 covcoeff<-as.numeric(covcoeff==1);covmy<-trunc(covmy)
 if ((covmy < 0) | (covmy > 2)) {covmy<-0} 
 boot<-abs(trunc(boot));mc=abs(trunc(mc));hc=trunc(hc)
 if ((intprobe < 0) | (intprobe > 1))
 {intprobe<-0.10}
 plot<-trunc(plot)
 if ((plot < 0) | (plot > 2)) {plot<-0}
 total<-as.numeric(total==1)
 dototal<-0
 saveboot<-as.numeric((save==1)|(save==3))
 saveest<-as.numeric(save > 1)
 if (saveest==1){intprobe=1}
 if ((hc >=0) & (hc < 5)) {notecode[notes,1]<-4;notes=notes+1}
 if ((hc > 5) | (hc < 0)) {hc=5}
 ####dont use these when y is dichotomous
 hclab<-matrix(c("se(HC0)","se(HC1)","se(HC2)","se(HC3)","se(HC4)","se"))
 hclab<-hclab[(hc+1),1]
 hcflab<-matrix(c("F(HC0)","F(HC1)","F(HC2)","F(HC3)","F(HC4)","F"))
 hcflab<-hcflab[(hc+1),1] 
 mcw=trunc(mcw);mcz=trunc(mcz);mcx=trunc(mcx);
 if ((mcx > 0) & (mcx < 3) &(model==74)) {mcw<-mcx;xscaling<-1}
 if ((mcx > 2) & (model==74))
  {errcode[errs,1]<-65;errs<-errs+1;criterr<-1}
 if ((model==74) & (normal==1))
  {notecode[notes,1]<-33;normal<-0;notes<-notes+1}
 if ((mcx > 0) & (contrast > 0))
 {notecode[notes,1]<-28;notes<-notes+1;contrast<-0}
 nxvls<-1;nmvls<-1;nwvls<-1;nzvls<-1
 paths<-matrix(999);pathsw<-matrix(999);pathsz<-matrix(999);
 pathswz<-matrix(999);pathsmod<-matrix(999);pathtype<-matrix(999);obscoeff<-999;
 pathsdv=matrix(" ");quantile<-1;moments=as.numeric(moments==1)
 if (moments==1){quantile<-0}
 bmatrix=t(matrix(bmatrix));wmatrix=t(matrix(wmatrix));zmatrix=t(matrix(zmatrix));
 wzmatrix=t(matrix(wzmatrix));cmatrix=t(matrix(cmatrix));xcatcode=t(matrix(xcatcode));
 wcatcode=t(matrix(wcatcode));zcatcode=t(matrix(zcatcode));
 needed<-0
 decimals<-paste("%",decimals,"f",sep='')
 if ((trunc(conf) >= 100) | (trunc(conf <= 50)))
  {conf=95;notecode[notes,1]<-2;notes=notes+1}
 if ((model >= 0)  & (model < 4) & (modelbt==0)) {boot<-0;mc<-0;bc<-0;saveboot<-0}
 if ((boot > 0) & (mc > 0)) {boot<-0;bc<-0}
 if ((boot < 1000) & (boot > 0) & (mc==0)){boot=5000}
 if ((mc < 1000) & (mc > 0) & (boot==0)){mc=5000}

#if ((model > 0) & (model < 4) & (boot = 98765) & (modelbt=1)) then;do;mc=0;boot=5000;end;
#if ((model > 0) & (model < 4) & ((boot = 98765) | (mc > 0))) then;do;boot=0;mc=0;bc=0;end;
#if ((boot = 98765) & (mc = 0)) then;do;boot=5000;end;

 p0<- -.322232431088;p1<- -1;p2<- -.342242088547;p3<- -.0204231210245;
 p4<- -.0000453642210148;q0<- .0993484626060;q1<- .588581570495;
 q2<- .531103462366;q3<- .103537752850;q4<- .0038560700634;
 priorlo<-(-9999999);priorhi<-9999999
 alpha2<-(1-(conf/100))/2;cilm<-alpha2*2;y5<-sqrt(-2*log(alpha2));
 xp2=(y5+((((y5*p4+p3)*y5+p2)*y5+p1)*y5+p0)/((((y5*q4+q3)*y5+q2)*y5+q1)*y5+q0))
 medlb=matrix(c("   M1 :","   M2 :","   M3 :","   M4 :","   M5 :","   M6 :","   M7 :","   M8 :","   M9 :","  M10 :"))
 medlb2=matrix(c("(M1)","(M2)","(M3)","(M4)","(M5)","(M6)","(M7)","(M8)","(M9)","(M10)"))
 xlb=matrix(c("X1 :","X2 :","X3 :","X4 :","X5 :","X6 :","X7 :","X8 :","X9 :","X10:"))
 highlbw=matrix(c("M1*W","M2*W","M3*W","M4*W","M5*W","M6*W","M7*W","M8*W","M9*W","M10*W"))
 if (xmint==1)
 {highlbw=matrix(c("M1*X","M2*X","M3*X","M4*X","M5*X","M6*X","M7*X","M8*X","M9*X","M10*X"))}
 highlbz=matrix(c("M1*Z","M2*Z","M3*Z","M4*Z","M5*Z","M6*Z","M7*Z","M8*Z","M9*Z","M10*Z"))
 highlbwz=matrix(c("M1*W*Z","M2*W*Z","M3*W*Z","M4*W*Z","M5*W*Z","M6*W*Z","M7*W*Z","M8*W*Z","M9*W*Z","M10*W*Z"))
 highlbx=matrix(c("M1*X","M2*X","M3*X","M4*X","M5*X","M6*X","M7*X","M8*X","M9*X","M10*X"))
 highlbbt=matrix(c("BOTH(M1)","BOTH(M2)","BOTH(M3)","BOTH(M4)","BOTH(M5)","BOTH(M6)","BOTH(M7)","BOTH(M8)","BOTH(M9)","BTH(M10)"))
 skipwz=0
 xnck=matrix(x);ynck=matrix(y);wnck=matrix(w);znck=matrix(z);mnck=matrix(m);cnck=matrix(cov)
 if ((nrow(xnck)>1) | (nrow(ynck)>1) | (nrow(wnck)>1) | (nrow(znck)>1))
  {errcode[errs,1]<-3;errs<-errs+1;criterr<-1}


 validm=matrix(c(1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,1,1,0,0,0,0,
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1))
 dim(validm)<-c(1,92)

 # check for major errors in syntax
 if (activate==1)
 {errcode[errs,1]<-60;errs<-errs+1;criterr<-1}
 if (criterr==0)
 {
  if ((model > 0) & (model < 93))
   {
    if (validm[1,model]==0)
    {
     errcode[errs,1]<-6;errs<-errs+1;criterr<-1
    }
   }
  if (((model > 92) | (model < 0)) & (model != 999))
   {errcode[errs,1]<-7;errs<-errs+1;criterr<-1}
  if ((model==999) & (bmatrix[1,1]==-999))
   {errcode[errs,1]<-24;errs<-errs+1;criterr<-1}
  if ((model != 999) & (bmatrix[1,1] != -999))
   {errcode[errs,1]<-25;errs<-errs+1;criterr<-1}
  if (((model==74) | ((model > 0) & (model < 4))) & ((wmatrix[1,1] !=-999) | (zmatrix[1,1] != -999) | (wzmatrix[1,1] != -999)))
    {errcode[errs,1]<-41;errs<-errs+1;criterr<-1}
  if ((y=="xxxxx") | (x=="xxxxx"))
    {errcode[errs,1]<-1;errs<-errs+1;criterr<-1}
  if ((mnck[1,1]=="xxxxx") & (model > 3))
    {errcode[errs,1]<-8;errs<-errs+1;criterr<-1}
 } 
 #end of check for major errors in syntax


 #A loop reads data and does some other things

 if (criterr==0)
 {
  #read y data
  ytmp<-data[y];nys<-ncol(ytmp);needed<-nys
  ynames<-matrix(y);n<-nrow(ytmp);varnames<-matrix(ynames)
  dat<-ytmp
  modelvar<-matrix(c(model,ynames))
  if (xmint==1)
  {modelvar[1,1]="4"}
  if (model==999){modelvar[1,1]="CUSTOM"}

  #read x data
  xtmp<-data[x];
  nxs<-ncol(xtmp);needed<-needed+nxs;
  dat<-cbind(dat,xtmp)
  xnames<-matrix(x);n<-nrow(xtmp);xcatlab<-xnames;
  varnames<-matrix(c(varnames,xnames))
  modelvar<-matrix(c(modelvar,xnames))
  if (nxs==1){modelvlb<-matrix(c("Model :","    Y :","    X :"))}

  #read m data
  if (mnck[1,1] != "xxxxx")
  {
   mtmp<-data[m]
   nms<-ncol(mtmp)
   mnames<-matrix(m)
   mprod<-matrix(0,1,nms)
   n<-nrow(mtmp)
   needed<-needed+nms
   varnames<-matrix(c(varnames,mnames))
   dat<-cbind(dat,mtmp)
   modelvar<-matrix(c(modelvar,mnames))
   x2m<-matrix(0,99,nms)
   x2m<-matrix(0,99,nms)
   x2y<-matrix(0,99,nms)
   onem<-matrix(1,nms,1)
   if ((nms > 1) & (nms < 11))
   {modelvlb<-matrix(c(modelvlb,medlb[1:nms,1]))}
   if (nms==1)
   {modelvlb<-matrix(c(modelvlb,"    M :"))}
   if ((nms > 0) & (model < 4)){errcode[errs,1]<-9;errs<-errs+1;criterr<-1}
  }

  #read w data
  wlocatet<-0;wlocate<-0
  if (w != "xxxxx")
  {
   if (xmint==0)
   {wtmp<-data[w];wnames<-matrix(w)}
   if (xmint==1)
   {wtmp<-data[x];wnames<-matrix(x)}
   nws=ncol(wtmp);n<-nrow(wtmp)
   dat<-cbind(dat,wtmp)
   wcatlab<-wnames
   varnames<-matrix(c(varnames,wnames))
   wlocate<-nrow(varnames)
   if (model==74)
   {
    wlocatet<-1;
    if (xnames != wnames)
    {errcode[errs,1]<-45;errs<-errs+1;criterr<-1}
   }
   if (xmint != 1)
   {modelvar<-matrix(c(modelvar,wnames))
   modelvlb<-matrix(c(modelvlb,"    W :"))}     
  }

  #read z data
  if (z != "xxxxx")
  {
   ztmp<-data[z];nzs<-ncol(ztmp);n<-nrow(ztmp);
   dat<-cbind(dat,ztmp)
   znames<-matrix(z);zcatlab<-znames;
   varnames<-matrix(c(varnames,znames))
   modelvar<-matrix(c(modelvar,znames))
   modelvlb<-matrix(c(modelvlb,"    Z :"))
  }

  #read cov data
  if (cnck[1,1] != "xxxxx")
  {
   ctmp<-data[cov];ncs<-ncol(ctmp);n<-nrow(ctmp);
   dat<-cbind(dat,ctmp)
   covnames<-matrix(cov);varnames<-matrix(c(varnames,covnames));  
  }
  
  if (((model==80) | (model==81)) & ((nms < 3) | (nms > 6)))
  {errcode[errs,1]<-32;errs<-errs+1;criterr<-1}
  if ((model==82) & (nms != 4))
  {errcode[errs,1]<-33;errs<-errs+1;criterr<-1}
  if (nms > 10)
  {errcode[errs,1]<-37;errs<-errs+1;criterr<-1}
  if (((model==6) | ((model > 82) & (model < 999))) & ((nms < 2) | (nms > 6)))
  {errcode[errs,1]<-34;errs<-errs+1;criterr<-1}

  #check for redundant variable names
  match<-0;match2<-0;mcwzcov<-0
  for (i in c(1:(nrow(varnames)-1)))
  {
   for (j in c((i+1):nrow(varnames)))
   {
    if (varnames[i]==varnames[j])
    {
     if (i < (nxs+nms+nys+1)){match2<-match2+1}
     if ((wlocatet==1) & (i==2) & (j==wlocate)){match2<-match2-1}
     if ((wnames==znames) & ((nws > 0) | (nzs > 0))){match<-match2+1} 
     if ((i < (nrow(varnames)-ncs+1)) & (j > (ncol(varnames)-ncs)))
     {
      if ((varnames[j]==wnames) & (mcw==0))
      {match<-0;wiscov<-(j-(nrow(varnames)-ncs))}
      if ((varnames[j]==wnames) & (mcw !=0)){mcwzcov<=0}
      if ((varnames[j]==znames) & (mcz==0))
      {match<-0;ziscov<-(j-nrow(varnames)-ncs)}
      if ((varnames[j]==znames) & (mcz !=0)){mcwzcov<-0}
     }
    }
   }
  }  
  if ((match2 > 0) | (match==1))
  {errcode[errs,1]<-2;errs<-errs+1;criterr<-1}
  if (mcwzcov==1)
  {errcode[errs,1]<-50;errs<-errs+1;criterr<-1}
 
  #check for factors and non numeric
  hello<-as.numeric(sapply(dat,is.factor))
  nfacs<-sum(hello)
  hello2<-as.numeric(sapply(dat,is.numeric))
  nnonnum<-sum(1-hello2)
  if ((nfacs > 0) | (nnonnum > 0)){errcode[errs,1]<-53;errs<-errs+1;criterr<-1}

  if (criterr==0)
  {

  #listwise deletion
  ninit<-nrow(dat);rownum<-seq(1:nrow(dat));rownumd<-matrix(0,nrow(dat),1);dat<-cbind(rownum,dat)
  datms<-is.na(dat)
  datms<-matrix(as.numeric(datms),nrow(datms))
  datms<-matrix(rowSums(datms))
  missrow<-0;delrow<-0;nmiss<-0;delident<-0;j<-1
  for (i in 1:ninit)
   {
    for (k in 1:nrow(deleteme))
    {if (deleteme[k,1]==i){rownumd[i,1]<-1}}
   }
   missrow<-matrix(missrow)
   for (i in 1:ninit)
   {
    delskip<-0;
    if ((rownumd[i,1]==1) & (delskip==0)){delskip<-1;delident<-1}
    if ((datms[i,1] > 0) & (delskip==0))
    {missrow<-cbind(missrow,i);nmiss<-nmiss+1;delskip<-1}
    if (delskip==0)
     {dat[j,]<-dat[i,];j<-(j+1)}      
   }   
   if (delident==1){notecode[notes,1]<-38;notes<-notes+1}
   missrow<-matrix(missrow)
   if (nrow(missrow) > 1)
   {
    missrow<-missrow[2:nrow(missrow)]
    notecode[notes,1]<-29;
    notes<-notes+1;
    missrow<-t(matrix(missrow))
   }  
  #dat<-na.omit(dat);n<-nrow(dat);
  if (j < 5)
  {errcode[errs,1]<-62;errs<-errs+1;criterr<-1}
  if (criterr==0)
  #startit
  {
  rownum<-matrix(dat[1:(j-1),1]);dat<-dat[1:(j-1),2:ncol(dat)]
  n<-nrow(dat)
  #extract the data back into vectors or matrices
  ytmp=as.data.frame(dat[,1:nys])
  desctmp2<-process.describ3(ytmp,0,quantile)
  desctmp<-matrix(unlist(desctmp2[1]))
  ysd<-desctmp[2,1];ovsd<-matrix(ysd);ydich<-0
  if (desctmp[8,1]==2){errcode[errs,1]<-15;errs<-errs+1;criterr<-1;novar<-1}
  if (desctmp[8,1]==1)
  {
   ydich<-1;
   if (total==1){total<-0;notecode[notes,1]<-24;notes<-notes+1}
   if (effsize==1){effsize<-0;notecode[notes,1]<-25;notes<-notes+1}
   if (model==74){errcode[errs,1]<-72;errs<-errs+1;criterr<-1}
   omx<-max(ytmp);omn<-min(ytmp)
   ytmp<-matrix(as.numeric(ytmp==omx),ncol=nys)
   dat[,1:nys]<-matrix(as.numeric(dat[,1:nys]==omx),ncol=nys)
   rcd<-c(omn,omx,0,1);dim(rcd)<-c(2,2)
  }
  xtmp<-as.data.frame(dat[,(nys+1):(nys+nxs)])
  desctmp2<-process.describ3(xtmp,0,quantile)
  desctmp<-matrix(unlist(desctmp2[1]))
  xsd<-desctmp[2,1]
  if ((desctmp[8,1]==2) & (novar==0))
   {errcode[errs,1]<-15;errs<-errs+1;criterr<-1;novar<-1}
  xmodvals<-matrix(unlist(desctmp2[2]))
  xdich<-desctmp[8,1]
  xmx=max(xtmp);xmn=min(xtmp)
  if ((mcx > 0) & (xrefvals[1,1] != 999) & (xmint==1) & (model== 74))
   {notecode[notes,1]<-36;notes=notes<-1}
  if (mcx==0)
  {
   if ((ncol(xrefvals)>2) & (model==74) & (xmint==1))
    {errcode[errs,1]<-67;errs<-errs+1;criterr<-1}
   if ((model==74) & (xmint==1))
   {
    if ((xrefvals[1,1]==999) & (nxvls==1) & (xdich==0))
     {errcode[errs,1]<-66;errs=errs<-1;criterr<-1}
    if ((xrefvals[1,1]==999) & (xdich==1))
     {xrefvals<-t(matrix(c(xmn,xmx)));xscaling<-xrefvals[1,2]-xrefvals[1,1]}
    if ((ncol(xrefvals)==1) & (xrefvals[1,1] != 999))
    {
     if (xdich==0)
     {
      xrefvals<-t(matrix(c(xrefvals,(xrefvals[1,1]+1))))
      xscaling<-xrefvals[1,2]-xrefvals[1,1]
     }
     if (xdich==1)
     {
      if ((xrefvals[1,1] != xmx) & (xrefvals[1,1] != xmn))
       {errcode[errs,1]<-70;errs<-errs+1;criterr<-1}
      if (xrefvals[1,1]==xmx)
       {xrefvals<-t(matrix(c(xrefvals,xmn)));xscaling<-xrefvals[1,2]-xrefvals[1,1]}
      if (xrefvals[1,1]==xmn)
       {xrefvals<-t(matrix(c(xrefvals,xmx)));xscaling<-xrefvals[1,2]-xrefvals[1,1]}
     }
    }
    if (ncol(xrefvals)==2)
    {
     xscaling<-xrefvals[1,2]-xrefvals[1,1]
     if (xdich==1)
     {
      xreferr<-1
      if (((xrefvals[1,1]==xmx) & (xrefvals[1,2]==xmn)) | ((xrefvals[1,1]==xmn) & (xrefvals[1,2]==xmx)))
       {xreferr<-0}
      if (xreferr==1)
       {errcode[errs,1]<-70;errs<-errs+1;criterr<-1}                
     }
    }
   }
  }
  if ((xmint==1) & (model==74) & (mcx==0))
  {xmodvals=t(xrefvals);xcontcf=1}
  nxpval<-nrow(xmodvals)
  xprobval<-as.matrix(xmodvals)
  if ((xdich==1) & (mcx > 0))
   {mcx<-0;errcode[errs,1]<-52;errs<-errs+1;criterr<-1}
  #if ((model==74) & (xdich==1)){xmint=1}
  if (nms > 0)
  {
   mtmp<-as.data.frame(dat[,(nys+nxs+1):(nys+nxs+nms)])
   desctmp2<-process.describ3(mtmp,0,quantile)
   desctmp<-matrix(unlist(desctmp2[1]),ncol=ncol(mtmp))
   ovsd<-matrix(c(desctmp[2,],ysd))
   medmeans<-cdeval;
   if ((cdeval[1,1] != -999) & (ncol(medmeans) != nms) & (model==74))
   {errcode[errs,1]<-64;errs<-errs+1;criterr<-1}
   if ((cdeval[1,1]==-999) & (model==74))     
      {medmeans<-t(matrix(desctmp[1,]))}
   if ((cdeval[1,1] != -999) & (model==74) & (criterr==0))
   {notecode[notes,1]<-31;notes<-notes+1}
   zzzz<-rowSums(desctmp)
   if ((zzzz[8]>0) & (mdichok != 1))
   {errcode[errs,1]<-43;errs<-errs+1;criterr<-1}   
   mmodvals<-matrix(unlist(desctmp2[2]),ncol=ncol(mtmp))
   mprobval<-mmodvals
  }
  

  if (nws > 0)
  {
   wtmp<-as.data.frame(dat[,(nys+nxs+nms+1):(nys+nxs+nms+nws)])
   desctmp2<-process.describ3(wtmp,0,quantile)
   desctmp<-matrix(unlist(desctmp2[1]))
   if ((desctmp[8,1]==2) & (novar==0))
   {errcode[errs,1]<-15;errs<-errs+1;criterr<-1;novar<-1}
   wmodvals<-matrix(unlist(desctmp2[2]))
   wdich<-desctmp[8,1]
   if ((wdich==1) & (mcw > 0))
   {mcw<-0;errcode[errs,1]<-52;errs<-errs+1;criterr<-1}
   wmin<-desctmp[3,1]
   wmax<-desctmp[4,1] 
   minwwarn<-matrix(unlist(desctmp2[3]));maxwwarn<-matrix(unlist(desctmp2[4]))
   wnotev<-matrix(unlist(desctmp2[5]))
   wmodval<-t(matrix(wmodval))
   if ((xmint==1) & (model==74) & (mcx==0))
   {wmodval<-xrefvals}
   nwcontr<-ncol(wmodval)
   if (wmodval[1,1] != 999)
   {
    wmodvals<-matrix(wmodval[1,1]);wmodcust<-1
    if (nwcontr > 1){wmodvals<-t(wmodval)}
    minwwarn<-0;maxwwarn<-0;wnotev<-0
   }
   wprobval<-as.matrix(wmodvals)
   nwpval<-nrow(wmodvals)
  }

  if (nzs > 0)
  {
   ztmp<-as.data.frame(dat[,(nys+nxs+nms+nws+1):(nys+nxs+nms+nws+nzs)])
   desctmp2<-process.describ3(ztmp,0,quantile)
   desctmp<-matrix(unlist(desctmp2[1]))
   if ((desctmp[8,1]==2) & (novar==0))
   {errcode[errs,1]<-15;errs<-errs+1;criterr<-1;novar<-1}
   zmodvals<-matrix(unlist(desctmp2[2]))
   zdich<-desctmp[8,1]
   if ((zdich==1) & (mcz > 0))
   {mcz<-0;errcode[errs,1]<-52;errs<-errs+1;criterr<-1}
   zmin<-desctmp[3,1]
   zmax<-desctmp[4,1] 
   minzwarn<-matrix(unlist(desctmp2[3]));maxzwarn<-matrix(unlist(desctmp2[4]));
   znotev<-matrix(unlist(desctmp2[5]))
   zmodval<-t(matrix(zmodval))
   nzcontr<-ncol(zmodval)
   if (zmodval[1,1] != 999)
   {
    zmodvals<-matrix(zmodval[1,1]);zmodcust<-1
    if (nzcontr > 1){zmodvals<-t(zmodval)}
    minzwarn<-0;maxzwarn<-0;znotev<-0
   }
   zprobval<-as.matrix(zmodvals)
   nzpval<-nrow(zmodvals)
  }
  if (ncs > 0)
   {
    ctmp<-as.data.frame(dat[,(nys+nxs+nms+nws+nzs+1):(nys+nxs+nms+nws+nzs+ncs)])
    covmean2<-process.describ3(ctmp,1,quantile)
    covmeans<-matrix(unlist(covmean2[1]),ncol=ncs);covmeans<-matrix(covmeans[1,],nrow=1)
   }
  coval<-t(matrix(coval))
  if ((coval[1,1] != -999) & (ncol(coval) != ncs) & (model==74))
  {errcode[errs,1]<-69;errs<-errs+1;criterr<-1}
  if ((coval[1,1] != -999) & (criterr==0) & (model==74))
  {notecode[notes,1]<-35;notes<-notes+1;cuscoval<-1}
  n<-nrow(ytmp)
  ones<-matrix(1,n,1)
  modresid<-matrix(9999,n,1)
  #creat codes for categorical variables
  if ((nws > 0) & (mcw > 0))
  {
   tmp<-cbind(rownum,wtmp[,1])
   dumtmp<-process.makdummy(tmp,mcw,2,wcatcode)
   wmodvals<-matrix(unlist(dumtmp[4]))
   nwpval<-nrow(wmodvals)
   errcode4<-unlist(dumtmp[5])
   errcode5<-unlist(dumtmp[6])
   errcode6<-unlist(dumtmp[7])
   if (errcode4==1){errcode[errs,1]<-4;errs<-errs+1;criterr<-1}
   if (errcode5==1){errcode[errs,1]<-5;errs<-errs+1;criterr<-1}
   if (errcode6==1){errcode[errs,1]<-39;errs<-errs+1;criterr<-1}   
   if (criterr==0)
   {
    nvls<-unlist(dumtmp[3])
    nwvls<-nvls-1
    minwwarn<-0;maxwwarn<-0;wnotev<-0
    wtmp<-matrix(unlist(dumtmp[1]),ncol=(nwvls+1))
    wtmp<-wtmp[,2:ncol(wtmp)];wtmp<-as.data.frame(wtmp)
    wcatlab<-c("W1","W2","W3","W4","W5","W6","W7","W8","W9")
    if (xmint==1)
    {wcatlab<-c("X1","X2","X3","X4","X5","X6","X7","X8","X9")}
    wcatlab<-matrix(wcatlab)
    mcwok<-1
    dummatw<-matrix(unlist(dumtmp[2]),ncol=(nwvls+1),nrow=(nwvls+1))
    wprobval<-dummatw[,2:ncol(dummatw)]
    if (modcok==1)
    {
     wcontval<-matrix(-999,2,ncol(wprobval))
     temp<-0
     for (i in (1:2))
     {
      for (j in (1:nrow(dummatw)))
      {
       if (contvec[i,1]==dummatw[j,1])
       {wcontval[i,]<-wprobval[j,];temp<-temp+1}
      }
     }
     if (temp < 2)
     {notecode[notes,1]<-20;notes<-notes+1;modcok<-0}
    }
    if ((wmodval[1,1] != 999) & (xmint != 1))
    {notecode[notes,1]<-9;notes<-notes+1}
   }
  }
  if ((nzs > 0) & (mcz > 0))
  {
   tmp<-cbind(rownum,ztmp[,1])
   dumtmp<-process.makdummy(tmp,mcz,3,zcatcode)
   zmodvals<-matrix(unlist(dumtmp[4]))
   nzpval<-nrow(zmodvals)
   errcode4<-unlist(dumtmp[5])
   errcode5<-unlist(dumtmp[6])
   errcode6<-unlist(dumtmp[7])
   if (errcode4==1){errcode[errs,1]<-4;errs<-errs+1;criterr<-1}
   if (errcode5==1){errcode[errs,1]<-5;errs<-errs+1;criterr<-1}
   if (errcode6==1){errcode[errs,1]<-40;errs<-errs+1;criterr<-1}
   if (criterr==0)
   {
    nvls<-unlist(dumtmp[3])
    nzvls<-nvls-1
    minzwarn<-0;maxzwarn<-0;znotev<-0
    ztmp<-matrix(unlist(dumtmp[1]),ncol=(nzvls+1))
    ztmp<-ztmp[,2:ncol(ztmp)];ztmp<-as.data.frame(ztmp)
    zcatlab<-c("Z1","Z2","Z3","Z4","Z5","Z6","Z7","Z8","Z9");zcatlab=matrix(zcatlab)
    mczok<-1
    dummatz<-matrix(unlist(dumtmp[2]),ncol=(nzvls+1),nrow=(nzvls+1))
    zprobval<-dummatz[,2:ncol(dummatz)]
    if (modcok==1)
    {
     zcontval<-matrix(-999,2,ncol(zprobval))
     temp<-0
     for (i in (1:2))
     {
      for (j in (1:nrow(dummatz)))
      {
       if (contvec[i,2]==dummatz[j,1])
       {zcontval[i,]<-zprobval[j,];temp<-temp+1}
      }
     }
     if (temp < 2)
     {notecode[notes,1]<-20;notes<-notes+1;modcok<-0}
    }
    if (zmodval[1,1] != 999)
    {notecode[notes,1]<-10;notes<-notes+1}
   }
  }
  if ((nxs > 0) & (mcx > 0))
  {
   tmp<-cbind(rownum,xtmp[,1])
   dumtmp<-process.makdummy(tmp,mcx,1,xcatcode)
   errcode4<-unlist(dumtmp[5])
   errcode5<-unlist(dumtmp[6])
   errcode6<-unlist(dumtmp[7])
   if (errcode4==1){errcode[errs,1]<-4;errs<-errs+1;criterr<-1}
   if (errcode5==1){errcode[errs,1]<-5;errs<-errs+1;criterr<-1}
   if (errcode6==1){errcode[errs,1]<-38;errs<-errs+1;criterr<-1}
   if (criterr==0)
   {
    nvls<-unlist(dumtmp[3])
    nxvls<-nvls-1
    xtmp<-matrix(unlist(dumtmp[1]),ncol=(nxvls+1))
    xtmp<-xtmp[,2:ncol(xtmp)];xtmp<-as.data.frame(xtmp)
    xcatlab<-c("X1","X2","X3","X4","X5","X6","X7","X8","X9");xcatlab=matrix(xcatlab)
    xdich<-as.numeric(nvls==2)
    mcxok<-1
    dummatx<-matrix(unlist(dumtmp[2]),ncol=(nxvls+1),nrow=(nxvls+1))
    xmodvals<-as.matrix(dummatx[,1])
    nxpval<-nrow(xmodvals)
   }
  }
  intlab<-matrix(" ",100,1)
  for (i in c(1:100)){intlab[i,1]<-paste("Int_",i,sep='')}
  bcmat<-matrix(0,needed,needed)
  wcmat<-matrix(0,needed,needed)
  zcmat<-matrix(0,needed,needed)
  wzcmat<-matrix(0,needed,needed)
  wsum<-0;zsum<-0;wzsum<-0
 }
 }

 }
 #endit

 # End A loop reads data and does some other things

 # Define model matrices for canned models
 if ((criterr==0) & (model != 999))
 { 
  # X->MW, X->MZ, X->MWZ, M->YW, M->YZ, M->YWZ, X->YW, X->YZ, X->YWZ
  modelmat<-
  matrix(c(1,0,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,1,1,0,3,0,0,0,0,0,0,1,1,1,4,0,0,0,0,0,0,0,0,0,
  5,0,0,0,0,0,0,1,0,0,6,0,0,0,0,0,0,0,0,0,7,1,0,0,0,0,0,0,0,0,8,1,0,0,0,0,0,1,0,0,
  9,1,1,0,0,0,0,0,0,0,10,1,1,0,0,0,0,1,1,0,11,1,1,1,0,0,0,0,0,0,12,1,1,1,0,0,0,1,1,1,
  13,1,1,1,0,0,0,1,0,0,14,0,0,0,1,0,0,0,0,0,15,0,0,0,1,0,0,1,0,0,16,0,0,0,1,1,0,0,0,0,
  17,0,0,0,1,1,0,1,1,0,18,0,0,0,1,1,1,0,0,0,19,0,0,0,1,1,1,1,1,1,20,0,0,0,1,1,1,1,0,0,
  21,1,0,0,0,1,0,0,0,0,22,1,0,0,0,1,0,1,0,0,23,0,0,0,0,0,0,0,0,0,24,0,0,0,0,0,0,0,0,0,
  25,0,0,0,0,0,0,0,0,0,26,0,0,0,0,0,0,0,0,0,27,0,0,0,0,0,0,0,0,0,28,1,0,0,0,1,0,0,1,0,
  29,1,0,0,0,1,0,1,1,0,30,0,0,0,0,0,0,0,0,0,31,0,0,0,0,0,0,0,0,0,32,0,0,0,0,0,0,0,0,0,
  33,0,0,0,0,0,0,0,0,0,34,0,0,0,0,0,0,0,0,0,35,0,0,0,0,0,0,0,0,0,36,0,0,0,0,0,0,0,0,0,
  37,0,0,0,0,0,0,0,0,0,38,0,0,0,0,0,0,0,0,0,39,0,0,0,0,0,0,0,0,0,40,0,0,0,0,0,0,0,0,0,
  41,0,0,0,0,0,0,0,0,0,42,0,0,0,0,0,0,0,0,0,43,0,0,0,0,0,0,0,0,0,44,0,0,0,0,0,0,0,0,0,
  45,0,0,0,0,0,0,0,0,0,46,0,0,0,0,0,0,0,0,0,47,0,0,0,0,0,0,0,0,0,48,0,0,0,0,0,0,0,0,0,
  49,0,0,0,0,0,0,0,0,0,50,0,0,0,0,0,0,0,0,0,51,0,0,0,0,0,0,0,0,0,52,0,0,0,0,0,0,0,0,0,
  53,0,0,0,0,0,0,0,0,0,54,0,0,0,0,0,0,0,0,0,55,0,0,0,0,0,0,0,0,0,56,0,0,0,0,0,0,0,0,0,
  57,0,0,0,0,0,0,0,0,0,58,1,0,0,1,0,0,0,0,0,59,1,0,0,1,0,0,1,0,0,60,1,1,0,1,0,0,0,0,0,
  61,1,1,0,1,0,0,1,0,0,62,1,1,0,1,0,0,0,1,0,63,1,1,0,1,0,0,1,1,0,64,1,0,0,1,1,0,0,0,0,
  65,1,0,0,1,1,0,1,0,0,66,1,0,0,1,1,0,0,1,0,67,1,0,0,1,1,0,1,1,0,68,1,1,1,1,0,0,0,0,0,
  69,1,1,1,1,0,0,1,1,1,70,1,0,0,1,1,1,0,0,0,71,1,0,0,1,1,1,1,1,1,72,1,1,1,1,1,1,0,0,0,
  73,1,1,1,1,1,1,1,1,1,74,0,0,0,1,0,0,0,0,0,75,1,1,0,1,1,0,0,0,0,76,1,1,0,1,1,0,1,1,0,
  77,0,0,0,0,0,0,0,0,0,78,0,0,0,0,0,0,0,0,0,79,0,0,0,0,0,0,0,0,0,80,0,0,0,0,0,0,0,0,0,
  81,0,0,0,0,0,0,0,0,0,82,0,0,0,0,0,0,0,0,0,83,1,0,0,0,0,0,0,0,0,84,1,0,0,0,0,0,0,0,0,
  85,1,0,0,0,0,0,1,0,0,86,1,0,0,0,0,0,1,0,0,87,0,0,0,1,0,0,0,0,0,88,0,0,0,1,0,0,0,0,0,
  89,0,0,0,1,0,0,1,0,0,90,0,0,0,1,0,0,1,0,0,91,0,0,0,0,0,0,0,0,0,92,1,0,0,1,0,0,1,0,0))
  dim(modelmat)<-c(10,92);modelmat=t(modelmat);
  if (model > 0){tmp<-modelmat[model,2:ncol(modelmat)];tmp=t(matrix(tmp))}
  if (model == 0){tmp<-matrix(0,1,9)}
  if (model < 4) {bcmat[(nxs+1),1]<-1}
  if ((model > 3) & (model != 6))
   {bcmat[(nxs+1):(nxs+nms),1]<-onem
    bcmat[nrow(bcmat),(nxs+1):(nxs+nms)]<-t(onem)
    bcmat[nrow(bcmat),1]<-1}
  if ((model==6) | ((model > 82) & (model < 93)))
  {
   for (j in c(2:nrow(bcmat)))
   {for (i in c(1:(j-1)))
     {bcmat[j,i]<-1}
   }
  } 
  if (model==80)
  {for (i in c(1:nms)){bcmat[(nrow(bcmat)-1),i]<-1}}
  if (model==81)
  {for (j in c(3:nrow(bcmat))){bcmat[j,2]<-1}}
  if (model==82)
  {bcmat[3,2]<-1;bcmat[5,4]<-1}
  if (tmp[1,1]==1)
  {
   wcmat[(nxs+1):(nxs+nms),1]=onem;wprod<-1;xprod<-1
   if ((model==83) | (model==86))
   {onemsx<-onem
    for (i in c(1:(nms-1))){onemsx[(i+1),1]<-0}
    wcmat[(nxs+1):(nxs+nms),1]<-onemsx
   } 
  }
  if (tmp[1,4]==1)
  {
   wcmat[nrow(wcmat),(nxs+1):(nxs+nms)]<-t(onem);wprod<-1;
   if ((model==87) | (model==90))
   {onemsx<-onem
    for (i in c(1:(nms-1))){onemsx[i,1]=0}
    wcmat[nrow(wcmat),(nxs+1):(nxs+nms)]=t(onemsx)
   }
  }
  if (tmp[1,7]==1){wcmat[nrow(wcmat),1]<-1;wprod<-1;xprod<-1}
  if (tmp[1,2]==1){zcmat[(nxs+1):(nxs+nms),1]<-onem;zprod<-1;xprod<-1}
  if (tmp[1,5]==1){zcmat[nrow(zcmat),(nxs+1):(nxs+nms)]=t(onem);zprod<-1}
  if (tmp[1,8]==1){zcmat[nrow(zcmat),1]<-1;zprod<-1;xprod<-1}
  if (tmp[1,3]==1){wzcmat[(nxs+1):(nxs+nms),1]<-onem;xprod<-1;wprod<-1;zprod<-1}
  if (tmp[1,6]==1){wzcmat[nrow(wzcmat),(nxs+1):(nxs+nms)]<-t(onem)}
  if (tmp[1,9]==1){wzcmat[nrow(wzcmat),1]<-1;xprod<-1;wprod<-1;zprod<-1}
  if ((model==91) | (model==92))
  {
   for (j in c(1:(nms-1)))
   {for (i in c(1:j)){wcmat[(nxs+1+j),(nxs+i)]<-1}
  }
 }
 }
 #define and check covariates matrix
 if (ncs > 0)
  {
   ccmat<-matrix(1,(nms+nys),ncs);ccmatoff<-ccmat
   if (covmy==1){ccmat[nrow(ccmat),]<-matrix(0,1,ncs)}
   if (covmy==2){ccmat[1:nms,]<-matrix(0,nms,ncs)}
   if (cmatrix[1,1] != -999)
    {if (ncol(cmatrix) != ((nms+nys)*ncs)){errcode[errs,1]<-29;errs<-errs+1;criterr<-1}
    if (criterr==0)
    {
      tmp<-1;for (i in c(1:(nms+nys)))
       {
       for (j in c(1:ncs))
       {ccmat[i,j]<-(1-as.numeric(cmatrix[1,tmp]==0));tmp<-tmp+1}
       }
      tmpcov<-colSums(ccmat);tmpcov<-as.numeric(tmpcov==0);tmpcov<-sum(tmpcov)
    if (tmpcov !=0)
    {errcode[errs,1]<-30;errs<-errs+1;criterr<-1}
    }
   if (covmy != 0){notecode[notes,1]<-1;notes<-notes+1}
  } 
 } 

 #Define matrices for custom models and do some error checking
 if (criterr==0)
 {
  needed<-needed*(needed-1)/2;nopath<-0
  if (bmatrix[1,1] != -999)
  {tmp<-1
   if ((ncol(bmatrix) != needed) | (sum(bmatrix)==0))
    {errcode[errs,1]<-16;errs<-errs+1;criterr<-1} else {
    for (i in c(2:nrow(bcmat)))
    {for (j in c(1:(i-1)))
     {bcmat[i,j]<-(1-as.numeric(bmatrix[1,tmp]==0));tmp<-tmp+1}        
    } 
   }
   #check to make sure X affects something */
   tmpcov<-sum(bcmat[,1])
   if ((tmpcov==0) & (criterr==0))
   {errcode[errs,1]<-22;errs<-errs+1;criterr<-1}
   #check to make sure Y is affected by something */
   tmpcov<-sum(bcmat[nrow(bcmat),])
   if ((tmpcov==0) & (criterr==0))
   {errcode[errs,1]<-23;errs<-errs+1;criterr<-1}
   #check for dangling mediators
   dm<-0
   if (nms > 0)
   {
    for (i in c(1:nms))
    tmpcov<-sum(bcmat[(nxs+i),]);tmpcov2<-sum(bcmat[,(nxs+i)])
    {if (((tmpcov==0) | (tmpcov2==0)) & (dm==0) & (criterr==0))
     {errcode[errs,1]<-26;errs<-errs+1;criterr<-1;dm<-1}       
    }
   }
  }
 }

 #start b
 if (criterr==0)
 {
  if (wmatrix[1,1] != -999)
  {
   tmp<-1
   if (ncol(wmatrix) != needed)
    {errcode[errs,1]<-17;errs<-errs+1;criterr<-1} else { 
     modelvar[1,1]="CUSTOM"
     for (i in (2:nrow(wcmat)))
     {     
      for (j in (1:(i-1)))       
      {wcmat[i,j]<-(1-as.numeric(wmatrix[1,tmp]==0))
       #dont allow to specify moderation of a path that doesnt exist
       if ((wcmat[i,j]==1) & (bcmat[i,j]==0) & (nopath==0))
       {errcode[errs,1]<-20;errs<-errs+1;criterr<-1;nopath<-1}   
       tmp<-tmp+1
      }
     } 
    }
  }

  if (zmatrix[1,1] != -999)
  {
   tmp<-1
   if (ncol(zmatrix) != needed)
    {errcode[errs,1]<-18;errs<-errs+1;criterr<-1} else {
     modelvar[1,1]="CUSTOM"
     if ((sum(wcmat)==0) & (model==999))
     {errcode[errs,1]<-21;errs<-errs+1;criterr<-1}
     for (i in (2:nrow(zcmat)))
     {     
      for (j in (1:(i-1)))       
      {zcmat[i,j]<-(1-as.numeric(zmatrix[1,tmp]==0))
       #dont allow to specify moderation of a path that doesnt exist
       if ((zcmat[i,j]==1) & (bcmat[i,j]==0) & (nopath==0))
       {errcode[errs,1]<-20;errs<-errs+1;criterr<-1;nopath<-1}   
       tmp<-tmp+1
      }
     } 
    }
  }
  tmp<-1
  if (wzmatrix[1,1] != -999)
  {
   if (ncol(wzmatrix) != needed)
   {errcode[errs,1]<-19;errs<-errs+1;criterr<-1}
   modelvar[1,1]="CUSTOM"
  } 
  if (criterr==0)
  {
   for (i in (2:nrow(wzcmat)))
   {
    for (j in (1:(i-1)))
    {
     #set corresponding elements in W and Z for three way interaction
     if (wzmatrix[1,1] != -999){wzcmat[i,j]<-(1-as.numeric(wzmatrix[1,tmp]==0))}
     if (wzcmat[i,j]==1){wcmat[i,j]<-1;zcmat[i,j]<-1}       
     #dont allow to specify moderation of a path that doesnt exist
     if ((wzcmat[i,j]==1) & (bcmat[i,j]==0) & (nopath==0))
     {errcode[errs,1]<-20;errs<-errs+1;criterr<-1;nopath<-1}
     tmp<-tmp+1
    }
   }
  }
 }
 # end B

 if (criterr==0)
 {
  xprod<-(sum(wcmat[,1])+sum(zcmat[,1])+sum(wzcmat[,1]))
  xprod<-as.numeric(xprod > 0)
  wsum<-sum(wcmat)
  wprod<-as.numeric(wsum > 0)
  if (nms > 0)
  {
   for (i in c(1:nms))
   {
    tmp<-(sum(wcmat[,(1+i)])+sum(zcmat[,(1+i)])+sum(wzcmat[,(1+i)]))
    mprod[1,i]<-as.numeric(tmp>0)
   }
  }
  if ((wsum > 0) & (w=="xxxxx")){errcode[errs,1]<-11;errs<-errs+1;criterr<-1}
  if ((wsum==0) & (w != "xxxxx")){errcode[errs,1]<-10;errs<-errs+1;criterr<-1}
  zsum<-sum(zcmat)
  zprod<-as.numeric(zsum > 0)
  if ((zsum > 0) & (z=="xxxxx")){errcode[errs,1]<-13;errs<-errs+1;criterr<-1}
  if ((zsum==0) & (z != "xxxxx")){errcode[errs,1]<-12;errs<-errs+1;criterr<-1}
  if ((zsum > 0) & (wsum==0)){errcode[errs,1]<-35;errs<-errs+1;criterr<-1}
 }
 if ((criterr==0) & (nms > 1))
 {
  serchk<-bcmat[2:(nrow(bcmat)-1),2:ncol(bcmat)]
  if (sum(serchk) > 0)
  {serial<-1
   if (nms > 6){errcode[errs,1]<-36;errs<-errs+1;criterr<-1}
  }
 }
 #mean center if needed
 if ((center > 0) & (criterr==0))
 {
  centvar<-matrix(c(" "))
  if (criterr==0)
  {
    if ((center==1) | ((center==2) & (wdich==0)))
    {
     if ((wprod==1) & (mcwok==0) & (nwpval > 0))
     {
      for (i in c(1:nws))
      {
       wtmp[,i]<-wtmp[,i]-(sum(wtmp[,i])/n)
       centvar<-cbind(centvar,wnames[1,i])
      }
      desctmp2<-process.describ3(wtmp,wmodcust,quantile)
      desctmp<-matrix(unlist(desctmp2[1]))
      wmin<-desctmp[3,1];wmax<-desctmp[4,1]
      if (wmodcust==0)
       {modvals<-matrix(unlist(desctmp2[2]))
        wmodvals<-modvals;wprobval<-wmodvals}
     }
    }    
    if ((center==1) | ((center==2) & (zdich==0)))
    { 
     if ((zprod==1) & (mczok==0) & (nzpval > 0))
     {
      for (i in c(1:nzs))
      {
       ztmp[,i]<-ztmp[,i]-(sum(ztmp[,i])/n)
       centvar<-cbind(centvar,znames[1,i])
      }
      desctmp2<-process.describ3(ztmp,zmodcust,quantile)
      desctmp<-matrix(unlist(desctmp2[1]))
      zmin<-desctmp[3,1];zmax<-desctmp[4,1]
      if (zmodcust==0)
       {modvals<-matrix(unlist(desctmp2[2]))
        zmodvals<-modvals;zprobval<-zmodvals}
     }
    }
    if ((center==1) | ((center==2) & (xdich==0)))
    {
     if ((xprod==1) & (mcxok==0))
     {
      for (i in c(1:nxs))
      {
       xtmp[,i]<-xtmp[,i]-(sum(xtmp[,i])/n)
       centvar<-cbind(centvar,xnames[1,i])
      }
      desctmp2<-process.describ3(xtmp,0,quantile)
      modvals<-matrix(unlist(desctmp2[2]))
      xmodvals<-modvals;xprobval<-as.matrix(xmodvals)
     }
    }
    if (nms > 0)
    {
     for (i in c(1:nms))
     {
       if (mprod[1,i]==1)
       {
        mtmp[,i]<-mtmp[,i]-(sum(mtmp[,i])/n)
        centvar<-cbind(centvar,mnames[i,1])
       }
     }
     desctmp2<-process.describ3(mtmp,0,quantile)
     desct12<-matrix(unlist(desctmp2[1]),ncol=ncol(mtmp))
     if ((cdeval[1,1]==-999) & (model==74))
      {medmeans<-t(matrix(desct12[1,]))}
     modvals<-matrix(unlist(desctmp2[2]),ncol=ncol(mtmp))
     mmodvals<-modvals;mprobval<-mmodvals;
    }
  }
  if (ncol(centvar) > 1)
  {notecode[notes,1]<-3;notes<-notes+1}
 }


 # Start D
 #CONSTRUCT THE DATA MATRICES FOR EACH OF THE MODELS
 if (criterr==0)
 {
  # The i loop is the dependent variable in the model matrices
  # The j loop is the predictor variables in the model matrices
  wsum<-sum(wcmat);zsum<-sum(zcmat);wzsum<-sum(wzcmat)
  nump<-matrix(-999,1,(nys+nms));numint<-matrix(0,1,(nys+nms))
  #DV
  datcount<-1;
  xtmpuse<-0;wtmpuse<-0;ztmpuse<-0;xwtmpus<-0;xztmpus<-0;wztmpus<-0;xwztmpu<-0;
  xtmploc<- matrix(-999);wtmploc<- -999;xwtmplo<- -999;ztmploc<- -999
  xztmplo<- -999;wztmplo<- -999;xwztmplo<- -999
  vlabs<-" "
  if (ncs > 0){ctmpuse<-matrix(0,1,ncs)}
  if (nms > 0)
  {
   mtmpuse<-matrix(0,1,nms)
   mwtmpus<-matrix(0,1,nms)
   mztmpus<-matrix(0,1,nms)
   mwztmpu<-matrix(0,1,nms)
   mtmploc<-matrix(0,1,nms)
   mwtmplo<-matrix(-999,nwvls,nms)
   mztmplo<-matrix(-999,nzvls,nms)
   mwztmplo<-matrix(-999,(nwvls*nzvls),nms)
  }
  if (ncs > 0){ctmploc<-matrix(0,1,ncs)}
  fulldat<-matrix(1,n,1)
  datindx<-matrix(-999,1000,(nms+nys))
  wherew<-matrix(-999,2,(nms+nys));wherex<-matrix(-999,2,(nms+nys))
  wherez<-matrix(-999,2,(nms+nys));wherexw<-matrix(-999,2,(nms+nys))
  wherexz<-matrix(-999,2,(nms+nys));wherewz<-matrix(-999,2,(nms+nys))
  wherexwz<-matrix(-999,2,(nms+nys))
  if (nms > 0)
  {
    wherem<-matrix(-999,nms,(nms+nys))
    wheremw<-matrix(-999,(nms*2),(nms+nys))
    wheremz<-matrix(-999,(nms*2),(nms+nys))
    wheremwz<-matrix(-999,(nms*2),(nms+nys))
  }  
  wzhigh<-matrix(0,1000,(((nms+1)*(nms+2))/2))
  whigh<-matrix(0,1000,(((nms+1)*(nms+2))/2))
  zhigh<-matrix(0,1000,(((nms+1)*(nms+2))/2))
  fochigh<-matrix(0,1000,(((nms+1)*(nms+2))/2))
  xcoefloc<-matrix(c(1,2,3,4,5,6,7,8,9))
  intkey<-t(matrix(c(" ", " ", " ", " ", " ", " ", " ")))
  wzhighct<-0;whighct<-0;zhighct<-0;foccnt<-0
  if (nms > 0){mnames<-t(mnames)}
  if (ncs > 0){covnames<-t(covnames)}
  xtmp<-data.matrix(xtmp)

 #START DV LOOP
  for (i in (2:nrow(bcmat)))
  {  
   wdid<-0;zdid<-0;wzdid<-0;cntmp<-1;start<-1;
   if (i < nrow(bcmat))
   {outv<-mtmp[,(i-1)]  
   modlabel<-matrix(c(mnames[1,(i-1)],"constant"))}
   if (i==nrow(bcmat))
   {outv<-ytmp;modlabel<-matrix(c(ynames,"constant"))}
   # The j loop is the mediator
   # START MED

   for (j in (1:(i-1)))
   {
    foccnt<-foccnt+1
    if ((j==1) & (bcmat[i,j]==1))
    {
     outv<-cbind(outv,xtmp)
     modlabel<-matrix(c(modlabel,xcatlab[1:nxvls,1]))
     if (xtmpuse==0)
     {
      fulldat<-matrix(c(fulldat,xtmp),nrow=n)
      xtmpuse<-1
      for (k4 in (datcount:(datcount+(nxvls-1))))
      {xtmploc<-matrix(c(xtmploc,k4))}
      xtmploc<-matrix(xtmploc[2:nrow(xtmploc),1])
      datcount<-datcount+nxvls
     }
     datindx[start:(start+nrow(xtmploc)-1),(i-1)]<-xtmploc
     wherex[1,(i-1)]<-start+1
     wherex[2,(i-1)]<-start+nrow(xtmploc)-1+1
    #do if (model = 74)
    #end if
     onebl<-matrix(1,nrow(xtmploc),1)
     fochigh[(start+1):(start+nrow(xtmploc)),foccnt]<-onebl
     start<-start+nrow(xtmploc)
    }  
    if ((j > 1) & (bcmat[i,j]==1))
     {
      outv<-cbind(outv,mtmp[,(j-1)])
      modlabel<-matrix(c(modlabel,mnames[1,(j-1)]))
      if (mtmpuse[1,(j-1)]==0)
      {
       fulldat<-matrix(c(fulldat,mtmp[,(j-1)]),nrow=n)
       mtmpuse[1,(j-1)]<-1
       mtmploc[1,(j-1)]<-datcount
       datcount<-datcount+1
      }
      datindx[start:(start+nrow(mtmploc)-1),(i-1)]<-mtmploc[1,(j-1)]
      wherem[(j-1),(i-1)]<-start+1
      #onebl<-matrix(1,(nrow(mtmploc[1,(j-1)])),1)
      #onebl<-matrix(1,mtmploc[1,(j-1)],1)
      onebl<-1
      #ttt<-nrow(mtmploc[1,(j-1)])+start-1
      #fochigh[(start+1):(start+nrow(mtmploc[1,(j-1)])),foccnt]<-onebl
      #fochigh[(start+1):(start+mtmploc[1,(j-1)]),foccnt]<-onebl
      fochigh[(start+1):(start+1),foccnt]<-onebl
      #start<-start+mtmploc[1,(j-1)]
      start<-start+1
     }
   }
   #END MED

   #START W
   if (wsum > 0)
   wtmp<-data.matrix(wtmp)
   {
    for (j in c(1:(i-1)))
    {
     whighct<-whighct+1
     if ((j==1) & (wcmat[i,j]==1))
     {
      if (wdid==0)
      {
       outv<-cbind(outv,wtmp)
       if ((ncs > 0) & (wiscov > 0)){ccmatoff[(i-1),wiscov]<-0}
       modlabel<-matrix(c(modlabel,wcatlab[1:nwvls,1]))
       wdid<-1
       if (wtmpuse==0)
       {
         fulldat<-matrix(c(fulldat,wtmp),nrow=n)
         if ((ncs > 0) & (wiscov > 0)){ccmatoff[(i-1),wiscov]<-0}         
         wtmpuse<-1
         for (k4 in (datcount:(datcount+(nwvls-1))))
         {wtmploc<-matrix(c(wtmploc,k4))}    
         wtmploc<-matrix(wtmploc[2:nrow(wtmploc),1])
         datcount<-datcount+nwvls
       }
      }     
      datindx[start:(start+nrow(wtmploc)-1),(i-1)]<-wtmploc
      wherew[1,(i-1)]<-start+1
      wherew[2,(i-1)]<-start+nrow(wtmploc)-1+1
      start<-start+nrow(wtmploc)
      for (k1 in c(1:nxvls))
      {
       for (k2 in c(1:nwvls))
       {
        outv<-as.matrix(cbind(outv,(xtmp[,k1]*wtmp[,k2])))
        if ((ncs > 0) & (wiscov > 0)){ccmatoff[(i-1),wiscov]<-0}          
        modlabel<-matrix(c(modlabel,intlab[cntmp,1]))
        intkeyt<-matrix(c(intlab[cntmp,1],":",xcatlab[k1,1],"x",wcatlab[k2,1]," "," "),ncol=7)
        intkey<-rbind(intkey,intkeyt)
        cntmp<-cntmp+1
       }
      }
      if (xwtmpus==0)
      {
       fulldat<-matrix(c(fulldat,outv[,(ncol(outv)-(nxvls*nwvls)+1):ncol(outv)]),nrow=n)
       xwtmpus<-1
       if ((ncs > 0) & (wiscov > 0)){ccmatoff[(i-1),wiscov]<-0}
       for (k4 in (datcount:(datcount+((nwvls*nxvls)-1))))
       {xwtmplo<-matrix(c(xwtmplo,k4))}
       xwtmplo<-matrix(xwtmplo[2:nrow(xwtmplo),1])
       datcount<-datcount+(nxvls*nwvls)
      }
      datindx[start:(start+nrow(xwtmplo)-1),(i-1)]<-xwtmplo
      wherexw[1,(i-1)]<-start+1
      wherexw[2,(i-1)]<-start+nrow(xwtmplo)-1+1
      onebl<-matrix(1,nrow(xwtmplo),1)
      whigh[(start+1):(start+nrow(xwtmplo)),whighct]<-onebl
      start<-start+nrow(xwtmplo)
     }
     if ((j > 1) & (wcmat[i,j]==1))
     {
      if ((wdid==0) & (model != 74))
      {
       outv<-cbind(outv,wtmp)
       if ((ncs > 0) & (wiscov > 0)){ccmatoff[(i-1),wiscov]<-0}
       modlabel<-matrix(c(modlabel,wcatlab[1:nwvls,1]))
       wdid<-1
       if (wtmpuse==0)
       {
        fulldat<-matrix(c(fulldat,wtmp),nrow=n)
        if ((ncs > 0) & (wiscov > 0)){ccmatoff[(i-1),wiscov]<-0}
        wtmpuse<-1
        for (k4 in (datcount:(datcount+(nwvls-1))))
        {wtmploc<-matrix(c(wtmploc,k4))}    
        wtmploc<-matrix(wtmploc[2:nrow(wtmploc),1])
        datcount<-datcount+nwvls
       }
       datindx[start:(start+nrow(wtmploc)-1),(i-1)]<-wtmploc
       wherew[1,(i-1)]<-start+1
       wherew[2,(i-1)]<-start+nrow(wtmploc)-1+1
       start<-start+nrow(wtmploc)
      }         
      for (k2 in c(1:nwvls))
      {
       outv<-as.matrix(cbind(outv,(mtmp[,(j-1)]*wtmp[,k2])))
       if ((ncs > 0) & (wiscov > 0)){ccmatoff[(i-1),wiscov]<-0}
       modlabel<-matrix(c(modlabel,intlab[cntmp,1]))
       intkeyt<-matrix(c(intlab[cntmp,1],":",mnames[1,(j-1)],"x",wcatlab[k2,1]," "," "),ncol=7)
       intkey<-rbind(intkey,intkeyt)
       cntmp<-cntmp+1
      }
      if (mwtmpus[1,(j-1)]==0)
      {
       fulldat<-matrix(c(fulldat,outv[,(ncol(outv)-nwvls+1):ncol(outv)]),nrow=n)
       if ((ncs > 0) & (wiscov > 0)){ccmatoff[(i-1),wiscov]<-0}
       mwtmpus[1,(j-1)]<-1
       mw22<- -999
       for (k4 in (datcount:(datcount+(nwvls-1))))
       {mw22<-matrix(c(mw22,k4))}
       mwtmplo[,(j-1)]=matrix(mw22[2:nrow(mw22),1])
       datcount<-datcount+nwvls
      }
      datindx[start:(start+nrow(mwtmplo)-1),(i-1)]<-mwtmplo[,(j-1)]
      wheremw[((2*j)-3),(i-1)]<-start+1
      wheremw[((2*j)-2),(i-1)]<-start+nrow(mwtmplo)-1+1
      onebl<-matrix(1,nrow(mwtmplo),1)
      whigh[(start+1):(start+nrow(mwtmplo)),whighct]<-onebl
      start<-start+nrow(mwtmplo)
     }
    }
   }
   #END W
   #START Z
   if (zsum > 0)
   ztmp<-data.matrix(ztmp)
   {
    for (j in c(1:(i-1)))    
    {
     zhighct<-zhighct+1
     if ((j==1) & (zcmat[i,j]==1))
     {
      if (zdid==0)
      {
       outv<-cbind(outv,ztmp)
       if ((ncs > 0) & (ziscov > 0)){ccmatoff[(i-1),ziscov]<-0}         
       modlabel<-matrix(c(modlabel,zcatlab[1:nzvls,1]))
       zdid<-1
       if (ztmpuse==0)
       {
        fulldat<-matrix(c(fulldat,ztmp),nrow=n)
        if ((ncs > 0) & (ziscov > 0)){ccmatoff[(i-1),ziscov]<-0} 
        ztmpuse<-1
        for (k4 in (datcount:(datcount+(nzvls-1))))
        {ztmploc<-matrix(c(ztmploc,k4))}      
        ztmploc<-matrix(ztmploc[2:nrow(ztmploc),1])
        datcount<-datcount+nzvls
       }
      }   
      datindx[start:(start+nrow(ztmploc)-1),(i-1)]<-ztmploc
      wherez[1,(i-1)]<-start+1
      wherez[2,(i-1)]<-start+nrow(ztmploc)-1+1
      start<-start+nrow(ztmploc)
      for (k1 in c(1:nxvls))
      {for (k2 in c(1:nzvls))
       {        
        outv=as.matrix(cbind(outv,(xtmp[,k1]*ztmp[,k2])))
        if ((ncs > 0) & (ziscov > 0)){ccmatoff[(i-1),ziscov]<-0}
        modlabel<-matrix(c(modlabel,intlab[cntmp,1]))
        intkeyt<-matrix(c(intlab[cntmp,1],":",xcatlab[k1,1],"x",zcatlab[k2,1]," "," "),ncol=7)
        intkey<-rbind(intkey,intkeyt)
        cntmp<-cntmp+1
       }
      }
      if (xztmpus==0)
      {
       fulldat<-matrix(c(fulldat,outv[,(ncol(outv)-(nxvls*nzvls)+1):ncol(outv)]),nrow=n)
       if ((ncs > 0) & (ziscov > 0)){ccmatoff[(i-1),ziscov]<-0}
       xztmpus<-1
       for (k4 in (datcount:(datcount+((nzvls*nxvls)-1)))){xztmplo<-matrix(c(xztmplo,k4))}       
       xztmplo<-matrix(xztmplo[2:nrow(xztmplo),1])
       datcount<-datcount+(nxvls*nzvls)
      }
      datindx[start:(start+nrow(xztmplo)-1),(i-1)]<-xztmplo
      wherexz[1,(i-1)]<-start+1
      wherexz[2,(i-1)]<-start+nrow(xztmplo)-1+1
      onebl<-matrix(1,nrow(xztmplo),1)
      zhigh[(start+1):(start+nrow(xztmplo)),zhighct]<-onebl
      start<-start+nrow(xztmplo)
     }
     if ((j > 1) & (zcmat[i,j]==1))
     {
      if (zdid==0)
      {
       outv<-cbind(outv,ztmp)
       if ((ncs > 0) & (ziscov > 0)){ccmatoff[(i-1),ziscov]<-0}
       modlabel<-matrix(c(modlabel,zcatlab[1:nzvls,1]))
       zdid<-1
       if (ztmpuse==0)
       {
        fulldat<-matrix(c(fulldat,ztmp),nrow=n)
        if ((ncs > 0) & (ziscov > 0)){ccmatoff[(i-1),ziscov]<-0}
        ztmpuse<-1
        for (k4 in (datcount:(datcount+(nzvls-1))))
        {ztmploc<-matrix(c(ztmploc,k4))}
        ztmploc<-matrix(ztmploc[2:nrow(ztmploc),1])
        datcount<-datcount+nzvls
       }
       datindx[start:(start+nrow(ztmploc)-1),(i-1)]<-ztmploc
       wherez[1,(i-1)]<-start+1
       wherez[2,(i-1)]<-start+nrow(ztmploc)-1+1
       start<-start+nrow(ztmploc)
      }    
      for (k2 in (1:nzvls))
      {
       outv=as.matrix(cbind(outv,(mtmp[,(j-1)]*ztmp[,k2])))
       if ((ncs > 0) & (ziscov > 0)){ccmatoff[(i-1),ziscov]<-0}
       modlabel<-matrix(c(modlabel,intlab[cntmp,1]))
       intkeyt<-matrix(c(intlab[cntmp,1],":", mnames[1,(j-1)],"x",zcatlab[k2,1]," "," "),ncol=7)
       intkey<-rbind(intkey,intkeyt)
       cntmp<-cntmp+1
      }
      if (mztmpus[1,(j-1)]==0)
      {
       fulldat<-matrix(c(fulldat,outv[,(ncol(outv)-nzvls+1):ncol(outv)]),nrow=n)
       if ((ncs > 0) & (ziscov > 0)){ccmatoff[(i-1),ziscov]<-0}
       mztmpus[1,(j-1)]<-1
       mz22<- -999
       for (k4 in (datcount:(datcount+(nzvls-1)))){mz22<-matrix(c(mz22,k4))}
       mztmplo[,(j-1)]<-matrix(mz22[2:nrow(mz22),1])
       datcount<-datcount+nzvls
      }
      datindx[start:(start+nrow(mztmplo)-1),(i-1)]<-mztmplo[,(j-1)]
      wheremz[((2*j)-3),(i-1)]<-start+1
      wheremz[((2*j)-2),(i-1)]<-start+nrow(mztmplo)-1+1
      onebl<-matrix(1,nrow(mztmplo),1)
      zhigh[(start+1):(start+nrow(mztmplo)),zhighct]<-onebl
      start<-start+nrow(mztmplo)
     }
    }
   }
   #END Z

   #START WZ
   if (wzsum > 0)
   {  
    for (j in (1:(i-1)))
    {
     wzhighct<-wzhighct+1
     if ((j==1) & (wzcmat[i,j]==1))
     {
      if (wzdid==0)
      {
       for (k1 in (1:nwvls))
       {
        for (k2 in (1:nzvls))
        {
         outv<-as.matrix(cbind(outv,(wtmp[,k1]*ztmp[,k2]))) 
         if ((ncs > 0) & (ziscov > 0)){ccmatoff[(i-1),ziscov]<-0}
         if ((ncs > 0) & (wiscov > 0)){ccmatoff[(i-1),wiscov]<-0}
         modlabel<-matrix(c(modlabel,intlab[cntmp,1]))
         intkeyt<-matrix(c(intlab[cntmp,1],":",wcatlab[k1,1],"x",zcatlab[k2,1]," "," "),ncol=7)
         intkey<-rbind(intkey,intkeyt)
         cntmp<-cntmp+1
        }
       }
       if (wztmpus==0)
       {
        fulldat<-matrix(c(fulldat,outv[,(ncol(outv)-(nwvls*nzvls)+1):ncol(outv)]),nrow=n)
        if ((ncs > 0) & (ziscov > 0)){ccmatoff[(i-1),ziscov]<-0}  
        if ((ncs > 0) & (wiscov > 0)){ccmatoff[(i-1),wiscov]<-0}
        wztmpus<-1
        for (k4 in (datcount:(datcount+((nwvls*nzvls)-1))))
        {wztmplo<-matrix(c(wztmplo,k4))}   
        wztmplo<-matrix(wztmplo[2:nrow(wztmplo),1])
        datcount<-datcount+(nzvls*nwvls)
       }
       wzdid<-1         
      }
      datindx[start:(start+nrow(wztmplo)-1),(i-1)]<-wztmplo
      wherewz[1,(i-1)]<-start+1
      wherewz[2,(i-1)]<-start+nrow(wztmplo)-1+1
      start<-start+nrow(wztmplo)
      for (k1 in (1:nxvls))
      {
       for (k2 in (1:nwvls))
       {
        for (k3 in (1:nzvls))
        {
         outv<-as.matrix(cbind(outv,(xtmp[,k1]*wtmp[,k2]*ztmp[,k3]))) 
         if ((ncs > 0) & (ziscov > 0)){ccmatoff[(i-1),ziscov]<-0}  
         if ((ncs > 0) & (wiscov > 0)){ccmatoff((i-1),wiscov)<-0}    
         modlabel<-matrix(c(modlabel,intlab[cntmp,1]))
         intkeyt<-matrix(c(intlab[cntmp,1],":",xcatlab[k1,1],"x",wcatlab[k2,1],"x",zcatlab[k3,1]),ncol=7)
         intkey<-rbind(intkey,intkeyt) 
         cntmp<-cntmp+1 
        }
       }
      }
      if (xwztmpu==0)
      {
       fulldat<-matrix(c(fulldat,outv[,(ncol(outv)-(nxvls*nwvls*nzvls)+1):ncol(outv)]),nrow=n)
       if ((ncs > 0) & (ziscov > 0)){ccmatoff[(i-1),ziscov]<-0} 
       if ((ncs > 0) & (wiscov > 0)){ccmatoff((i-1),wiscov)<-0}
       xwztmpu<-1
       for (k4 in (datcount:(datcount+((nzvls*nxvls*nwvls)-1))))
       {xwztmplo<-matrix(c(xwztmplo,k4))}  
       xwztmplo<-matrix(xwztmplo[2:nrow(xwztmplo),1])
       datcount<-datcount+(nxvls*nzvls*nwvls)
      }
      datindx[start:(start+nrow(xwztmplo)-1),(i-1)]<-xwztmplo
      wherexwz[1,(i-1)]<-start+1
      wherexwz[2,(i-1)]<-start+nrow(xwztmplo)-1+1
      onebl<-matrix(1,nrow(xwztmplo),1)
      wzhigh[(start+1):(start+nrow(xwztmplo)),wzhighct]<-onebl
      start<-start+nrow(xwztmplo)
     }
     if ((j > 1) & (wzcmat[i,j]==1))
     {
      if (wzdid==0)
      {
       for (k1 in (1:nwvls))
       {
        for (k2 in (1:nzvls))
        {
         outv<-as.matrix(cbind(outv,(wtmp[,k1]*ztmp[,k2])))
         if ((ncs > 0) & (ziscov > 0)){ccmatoff[(i-1),ziscov]<-0}  
         if ((ncs > 0) & (wiscov > 0)){ccmatoff[(i-1),wiscov]<-0}
         modlabel<-matrix(c(modlabel,intlab[cntmp,1]))
         intkeyt<-matrix(c(intlab[cntmp,1],":",wcatlab[k1,1],"x",zcatlab[k2,1]," "," "),ncol=7)
         intkey<-rbind(intkey,intkeyt) 
         cntmp<-cntmp+1
        }
       }
       if (wztmpus==0)
       {
        fulldat<-matrix(c(fulldat,outv[,(ncol(outv)-(nwvls*nzvls)+1):ncol(outv)]),nrow=n)
        if ((ncs > 0) & (ziscov > 0)){ccmatoff[(i-1),ziscov]<-0}
        if ((ncs > 0) & (wiscov > 0)){ccmatoff[(i-1),wiscov]<-0}
        wztmpus<-1
        for (k4 in (datcount:(datcount+((nwvls*nzvls)-1))))
        {wztmplo<-matrix(c(wztmplo,k4))}    
        wztmplo<-matrix(wztmplo[2:nrow(wztmplo),1])
        datcount<-datcount+(nzvls*nwvls)
       }
       wzdid<-1
       datindx[start:(start+nrow(wztmplo)-1),(i-1)]<-wztmplo
       wherewz[1,(i-1)]<-start+1
       wherewz[2,(i-1)]<-start+nrow(wztmplo)-1+1
       start<-start+nrow(wztmplo)
      }
      for (k1 in (1:nwvls))
      {
       for (k2 in (1:nzvls))
       {
        outv<-as.matrix(cbind(outv,(mtmp[,(j-1)]*wtmp[,k1]*ztmp[,k2])))
        if ((ncs > 0) & (ziscov > 0)){ccmatoff[(i-1),ziscov]<-0}  
        if ((ncs > 0) & (wiscov > 0)){ccmatoff[(i-1),wiscov]<-0} 
        modlabel<-matrix(c(modlabel,intlab[cntmp,1]))
        intkeyt<-matrix(c(intlab[cntmp,1],":",mnames[1,(j-1)],"x",wcatlab[k1,1],"x",zcatlab[k2,1]),ncol=7)
        intkey<-rbind(intkey,intkeyt) 
        cntmp<-cntmp+1
       }
      }
      if (mwztmpu[1,(j-1)]==0)
      {
       fulldat<-matrix(c(fulldat,outv[,(ncol(outv)-(nwvls*nzvls)+1):ncol(outv)]),nrow=n)
       if ((ncs > 0) & (ziscov > 0)){ccmatoff[(i-1),ziscov]<-0} 
       if ((ncs > 0) & (wiscov > 0)){ccmatoff[(i-1),wiscov]<-0}
       mwztmpu[1,(j-1)]<-1
       mz22<- -999
       for (k4 in (datcount:(datcount+(nwvls*nzvls)-1)))
       {mz22<-matrix(c(mz22,k4))}
       mwztmplo[,(j-1)]<-matrix(mz22[2:nrow(mz22),1])
       datcount<-datcount+(nwvls*nzvls)
      }
      datindx[start:(start+nrow(mwztmplo)-1),(i-1)]<-mwztmplo[,(j-1)]
      wheremwz[((2*j)-3),(i-1)]<-start+1
      wheremwz[((2*j)-2),(i-1)]<-start+nrow(mwztmplo)-1+1
      onebl<-matrix(1,nrow(mwztmplo),1)
      wzhigh[(start+1):(start+nrow(mwztmplo)),wzhighct]<-onebl
      start<-start+nrow(mwztmplo)
     }
    }
   }
   #END WZ
   
   #START COV
   if (ncs > 0)
   {  
    ccmat<-ccmat*ccmatoff
    for (j in c(1:ncs))
    {
     if (ccmat[(i-1),j]==1)
     {
      if (j==wiscov){ctmp[,j]<-wtmp}
      if (j==ziscov){ctmp[,j]<-ztmp}
      outv<-cbind(outv,ctmp[,j])
      modlabel<-matrix(c(modlabel,covnames[1,j]))
      if (ctmpuse[1,j]==0)
      {
       fulldat<-matrix(c(fulldat,ctmp[,j]),nrow=n)
       ctmpuse[1,j]<-1
       ctmploc[1,j]<-datcount
       datcount<-datcount+1
      }
      datindx[start:(start+nrow(ctmploc)-1),(i-1)]<-ctmploc[1,j]
      start<-start+1
     }
    }
   }
   #END COV
   
   wdid<-0;zdid<-0;wzdid<-0
   vlabs<-matrix(c(vlabs,modlabel[2:nrow(modlabel),1]))
   numint[1,(i-1)]<-cntmp-1
   nump[1,(i-1)]<-nrow(modlabel)-1

  } 
  #END DV LOOP

  if ((modcok==1) & ((nms > 0) | (zcmat[2,1] != 1) | (mcx != 0)))
  {notecode[notes,1]<-19;notes<-notes + 1;modcok<-0}
  if (((serial==1) | (sum(numint)>0) | (nms==0)) & (mc > 0))
  {notecode[notes,1]<-15;notes<-notes+1;boot<-mc;mc<-0}


  if ((boot != 0) | (mc != 0))
  {
   bootsz<-boot
   if (mc > 0){bootsz<-mc;saveboot<-0}
   cilow<-0;cihigh<-bootsz+1
   while ((cilow <=0) | (cihigh > bootsz))
   {
    cilow<-round(bootsz*(1-(conf/100))/2)
    cihigh<-trunc((bootsz*(conf/100)+(bootsz*(1-(conf/100))/2)))+1
    if ((cilow < 1) | (cihigh > bootsz))
    {bootsz<-trunc((bootsz+1000)/1000)*1000;adjust<-1}
   }
   if (boot > 0){boot<-bootsz}
   if (mc > 0){mc<-bootsz}
   if ((adjust==1) & (boot > 0)){notecode[notes,1]<-8;notes<-notes+1}
   if ((adjust==1) & (mc > 0)){notecode[notes,1]<-16;notes<-notes+1}
  }
  maxboot<-trunc(2*boot)
  if (maxboots > maxboot){maxboot<-trunc(maxboots)}
  #insert seed here  
  if ((seed==-999) & (boot > 0) | (mc > 0))
  {seed<-trunc(runif(1,1,1000000));set.seed(seed);ranseed<-1}
  if (sum(numint) > 0){intkey<-matrix(intkey[2:nrow(intkey),],ncol=7)}
  vlabs<-matrix(vlabs[2:nrow(vlabs),1])
  fulldat<-as.data.frame(fulldat[,2:ncol(fulldat)])
  fulldat<-data.matrix(fulldat)
  fochigh<-fochigh[1:max(nump),]
  whigh<-as.matrix(whigh[1:max(nump),])
  zhigh<-as.matrix(zhigh[1:max(nump),])
  wzhigh<-as.matrix(wzhigh[1:max(nump),])
  coeffs<-fochigh+whigh+zhigh+wzhigh
  bootloc<-matrix(0,max(nump),ncol(nump))

  #Here is am deriving the locations in bootfile needed for indirect effects
  if (nms > 0)
  {
   cntmp<-1
   for (i in (1:ncol(nump)))
   {for (j in (1:nump[1,i]))
    {bootloc[j,i]<-cntmp;cntmp<-cntmp+1}
   }
   fochighb<-matrix(0,nrow(fochigh),ncol(fochigh))
   whighb<-fochighb
   zhighb<-fochighb
   wzhighb<-fochighb
   thetaxmb<-matrix(0,nrow(fochighb),nms)
   thetaxyb<-matrix(0,nrow(fochighb),1)
   pathsfoc<-matrix(0,nxvls,1)
   cntmp<-1
   for (i in (1:(nms+nys)))
   {
    for (j in (1:i))
    {
     fochighb[,cntmp]<-(fochigh[,cntmp]*bootloc[,i])
     whighb[,cntmp]<-(whigh[,cntmp]*bootloc[,i])
     zhighb[,cntmp]<-(zhigh[,cntmp]*bootloc[,i])
     wzhighb[,cntmp]<-(wzhigh[,cntmp]*bootloc[,i])
     coeffsb<-(fochighb+whighb+zhighb+wzhighb)
     if ((i < (nms+nys)) & (j==1)){thetaxmb[,i]<-coeffsb[,cntmp]}
     if ((i==(nms+nys)) & (j==1)){thetaxyb[,1]<-coeffsb[,cntmp]}
     cntmp<-cntmp+1
    }
   }
   thetamyb<-as.matrix(coeffsb[,(ncol(coeffsb)-nms+1):ncol(coeffsb)])
   if (serial==1)
   {thetammb<-matrix(0,nrow(coeffsb),((nms*(nms-1))/2))}
   cntmp<-1
   if ((nms > 1) & (serial==1))
   {
    for (i in (1:(nms-1)))
    {
     start<-((i+2)*(i+1))/2
     for (j in (2:(nms-i+1)))
     {
      thetammb[,cntmp]<-coeffsb[,start]
      start<-start+j+i-1
      cntmp<-cntmp+1
     }
    } 
   }
  }

  if ((total==1) & ((sum(numint)==0) | (xmint==1)))
  {
   dototal<-1
   if ((sum(bcmat[,1]) != (nms+nys)) | (sum(bcmat[nrow(bcmat),]) != (nms+nys)))
   {dototal<-0;alttotal<-1;notecode[notes,1]<-12;notes<-notes+1}
   if (ncs > 0)
   {
    if ((sum(ccmat)) < (nrow(ccmat)*ncol(ccmat)))
    {dototal<-0;alttotal<-1;notecode[notes,1]<-11;notes<-notes+1}
   }
   if (model==74)
   {
    if ((xdich==0) & (nxvls==1))
    {dototal<-0;alttotal<-1}
    if (((xdich==1) | (nxvls > 1)) & (ncs > 0) & (model==74))
    {dototal<-0;alttotal<-1}
   }
  }
 }
 # End D
 if ((criterr==0) & (ncs > 0))
 {
  tmperr<-sum(as.numeric(colSums(ccmat)==0))
  if (sum(tmperr)!=0)
  {errcode[errs,1]<-51;errs<-errs+1;criterr<-1}
 }
 if (outscreen==1)
 {
  cat("\n")
  cat("********************* PROCESS for R Version 4.3.1 ********************* \n \n")
  cat("           Written by Andrew F. Hayes, Ph.D.  www.afhayes.com              \n")
  cat("   Documentation available in Hayes (2022). www.guilford.com/p/hayes3   \n \n")
 }
 if (criterr==0)
 {
  if ((stand==1) & (ydich==1)){stand<-0}
  anymod2<-sum(wcmat+zcmat+wzcmat)
  if ((anymod2 > 0) & (stand==1))
  {notecode[notes,1]<-27;notes<-notes+1;stand<-0}
  rownames(modelvar)<-modelvlb
  colnames(modelvar)<-" "
  funny<-1
  if (outscreen==1)
  {
   cat("*********************************************************************** \n")
   print(noquote(modelvar))
   cat("\n")
   if (ncs > 0)
   {
   cat("Covariates: \n")
   covname2<-c("      ", covnames)
   covname2<-t(noquote(covname2))  
   write.table(covname2,quote=FALSE,row.names=FALSE,col.names=FALSE)
   cat("\n")
   }
   cat("Sample size: ")
   write.table(n,quote=FALSE,row.names=FALSE,col.names=FALSE)
   cat("\n")
   if (seed != -999)    
   {if (ranseed==0)
    {cat("Custom seed: ")}
    if (ranseed==1)
    {cat("Random seed: ")}
    write.table(seed,quote=FALSE,row.names=FALSE,col.names=FALSE)
    cat("\n")
   }
   #maxresm<-9;
   #resultm<-matrix(99999,1,maxresm)
  }  
   if (describe==1)
   {
    means=apply(dat,2,mean);sdvec=apply(dat,2,sd);corall=cor(dat);
    means<-rbind(t(matrix(means)),t(matrix(sdvec)))
    means2<-noquote(matrix(sprintf(decimals,means),nrow=nrow(means)))
    corall2<-noquote(matrix(sprintf(decimals,corall),nrow=nrow(corall)))
    #if (ncol(means) > 9)
    #{resultm<-matrix(99999,1,ncol(means));maxresm<-ncol(means)}
    outformres<-process.outform3(means,maxresm,resultm)
    maxresm<-unlist(outformres[2]);resultm<-matrix(unlist(outformres[1]),ncol=maxresm)
    outformres<-process.outform3(corall,maxresm,resultm)
    maxresm<-unlist(outformres[2]);resultm<-matrix(unlist(outformres[1]),ncol=maxresm)

    if (outscreen==1)
    {
     colnames(means2)<-varnames
     colnames(corall2)<-varnames
     rownames(corall2)<-varnames
     rownames(means2)<-matrix(c("Mean","SD"))
     cat("Variable means and standard deviations:\n")
     print(means2,right=T)
     cat("\n")
     cat("Variable intercorrelations (Pearson's r):\n")
     print(corall2,right=T)
    }     
   }

  if (outscreen==1)
  {
   if (mcxok==1)
   {cat("Coding of categorical X variable for analysis: \n")
   dummatx2<-noquote(matrix(sprintf(decimals,dummatx),nrow=nrow(dummatx)))
   colnames(dummatx2)<-c(xnames,xcatlab[1:nxvls,1])
   rownames(dummatx2)<-t(matrix(replicate((nxvls+1)," ")))
   print(dummatx2,right=T)}
   if ((mcwok==1) & (xmint==0))
   {cat("Coding of categorical W variable for analysis: \n")
   dummatw2<-noquote(matrix(sprintf(decimals,dummatw),nrow=nrow(dummatw)))
   colnames(dummatw2)<-c(wnames,t(wcatlab[1:nwvls,1]))
   rownames(dummatw2)<-t(matrix(replicate((nwvls+1)," ")))
   print(dummatw2,right=T)}
   if (mczok==1)
   {cat("Coding of categorical Z variable for analysis: \n")
   dummatz2<-noquote(matrix(sprintf(decimals,dummatz),nrow=nrow(dummatz)))
   colnames(dummatz2)<-c(znames,t(zcatlab[1:nzvls,1]))
   rownames(dummatz2)<-t(matrix(replicate((nzvls+1)," ")))
   print(dummatz2,right=T)}
  }
 } 
 #START cycle through the models  
 if (criterr==0)
 {
  outnames<-ynames
  outvars<-ytmp
  if (nms > 0)
  {
   outnames<-matrix(c(mnames,ynames))
   outvars<-cbind(mtmp,ytmp)
   indcov<-matrix(0,((nms*2)+(nms*(nxvls-1))),((nms*2)+(nms*(nxvls-1))))
   mcsopath<-matrix(0,((nms*2)+(nms*(nxvls-1))),1)
  }
  labstart<-1
  intstart<-1
  start<-1
  coeffmat<-matrix(0,1,6)
  conseq<-"        "
  dfmat<-0;coeffcol<-0;pathscnt<-1;pathscn2<-1

  # START G LOOP
  for (i in c(1:(nms+nys)))
  {
   if (outscreen==1)
   {cat("\n*********************************************************************** \n")}
   highf<-matrix(0,1,5);highf2<-highf
   if ((i==(nms+nys)) & (ydich==1)){highf=matrix(0,1,3);highf2<-highf}
   flabel<-" "   
   y<-outvars[,i]
   xindx<-datindx[1:(nump[1,i]-1),i]
   x<-fulldat[,xindx]
   x<-cbind(ones,x)
   xsq<-t(x)%*%x
   exsq<-eigen(xsq)
   exsq<-matrix(unlist(exsq[1]))
   zeroeig<-sum(as.numeric(exsq <= 0.000000000002))
   if (outscreen==1)
   {
    cat("Outcome Variable: ")
    write.table(outnames[i,1],quote=FALSE,row.names=FALSE,col.names=FALSE)
    cat("\n")
    if ((ydich==1) & (i==(nms+nys)))
    {
     cat("Coding of binary Y for logistic regression analysis:\n")
     rcd<-noquote(matrix(sprintf(decimals,rcd),nrow=nrow(rcd)))
     colnames(rcd)<-c(outnames[i,1], "Analysis")
     rownames(rcd)<-t(matrix(replicate(nrow(rcd)," ")))
     print(rcd,right=T)
     cat("\n")
    }
   }
   if (zeroeig > 0)
   {
    cat("\nSINGULAR OR NEAR SINGULAR DATA MATRIX.\n")
    criterr<-1;errcode[errs,1]<-31;errs<-errs+1;
   }    
   means<-colSums(x)/n
   vlabsm<-matrix(vlabs[labstart:(labstart+(nump[1,i]-1)),1])
   #START E
   if (criterr==0)
   {
    if ((ydich==0) | (i < (nms+nys)))
    {
     modoutz<-process.modelest(y,x,1,1,xp2,hc)
     modres<-matrix(unlist(modoutz[1]),nrow=nump[1,i])
     direff<-matrix(modres,nrow=ncol(x))
     #modresl<-matrix(unlist(modoutz[2]))  
     modsum<-matrix(unlist(modoutz[3]))
     tval<-matrix(unlist(modoutz[7]))
     resid<-matrix(unlist(modoutz[8]))
     modresid<-cbind(modresid,resid)
     brsq2<-modsum[2,1]
     dfres<-modsum[6,1]
     #for recording results
     outformres<-process.outform3(modsum,maxresm,resultm)
     maxresm<-unlist(outformres[2]);resultm<-matrix(unlist(outformres[1]),ncol=maxresm)
     outformres<-process.outform3(modres,maxresm,resultm)
     maxresm<-unlist(outformres[2]);resultm<-matrix(unlist(outformres[1]),ncol=maxresm)
     #end recording results
     modrest9<-modres
     modres<-noquote(matrix(sprintf(decimals,modres),nrow=nump[1,i]))
     modsum<-noquote(matrix(sprintf(decimals,modsum),nrow=1))
     b<-matrix(unlist(modoutz[5]))
     varb<-matrix(unlist(modoutz[6]),nrow=nump[1,i])
     dfmatt<-matrix(modsum[1,6],nrow(modres),1)
     modsuml<-matrix(c("R","R-sq","MSE",hcflab,"df1","df2", "p"))
     modresl<-t(matrix(c("coeff",hclab,"t","p","LLCI","ULCI")))
    }
    if ((ydich==1) & (i==(nms+nys)))
    {
     modoutz<-process.modelest(y,x,2,1,xp2,5,iterate,converge)
     modres<-matrix(unlist(modoutz[1]),nrow=nump[1,i])
     direff<-matrix(modres,nrow=ncol(x))
     modsum<-matrix(unlist(modoutz[3]))
     basemod<-modsum[1,1]
     basemodx<-basemod
     tval<-matrix(unlist(modoutz[7]))
     outformres<-process.outform3(modsum,maxresm,resultm)
     maxresm<-unlist(outformres[2]);resultm<-matrix(unlist(outformres[1]),ncol=maxresm)
     outformres<-process.outform3(modres,maxresm,resultm)
     maxresm<-unlist(outformres[2]);resultm<-matrix(unlist(outformres[1]),ncol=maxresm)
     modrest9<-modres
     modres<-noquote(matrix(sprintf(decimals,modres),nrow=nump[1,i]))
     modsum<-noquote(matrix(sprintf(decimals,modsum),nrow=1))
     b<-matrix(unlist(modoutz[5]))
     varb<-matrix(unlist(modoutz[6]),nrow=nump[1,i])
     modsuml<-matrix(c("-2LL","ModelLL", "df", "p", "McFadden", "CoxSnell", "Nagelkrk"))
     modresl<-t(matrix(c("coeff","se","Z","p","LLCI","ULCI")))
     #modretrn<-list(modres,modresl,modsum,modsuml,b,varb,xp2)
     dfmatt<-matrix(-999,nrow(modres),1)
    } 
    obscoeff<-cbind(obscoeff,t(b))
    if (outscreen==1)
    {
     cat("Model Summary: \n")
     colnames(modsum)<-modsuml;rownames(modsum)<-" "
     print(modsum,right=T)
     cat("\n")
     cat("Model: \n")
     rownames(modres)<-vlabsm;colnames(modres)<-modresl
     print(modres,right=T)   
     if ((ydich==1) & (i==(nms+nys)))
     {
      cat("\nThese results are expressed in a log-odds metric.\n")
      notecode[notes,1]<-26;notes<-notes+1
     }
    }
    coeffmat<-rbind(coeffmat,modres)
    conseqt<-matrix(outnames[i,1],nrow(modres),1)
    conseq<-matrix(c(conseq,conseqt))
    dfmat<-rbind(dfmat,dfmatt)
    labstart<-labstart+nump[1,i] 

    if (stand==1)
    {
     predsd<-matrix(0,nrow(modres),1)
     stdmod<-as.numeric(modres[,1])/ovsd[i,1]
     for (jd in c(1:ncol(x)))
     {
     predsd[jd,1]<-sd(x[,jd])
     }
     if ((wherex[1,i] != -999) & ((nxvls > 1) | (xdich==1)))    
     {
      sdmsone<-matrix(1,nxvls,1)
      predsd[wherex[1,i]:wherex[2,i],1]<-sdmsone    
      pstog<-1    
     }
     predsd[1,1]<-1
     stdmod<-stdmod*predsd
     stdmod<-matrix(stdmod[2:nrow(stdmod),1])
     sdvlabs<-vlabsm[2:nrow(vlabsm),1]
     if (outscreen==1){cat("\nStandardized coefficients:\n")}
     outformres<-process.outform3(stdmod,maxresm,resultm,1)
     maxresm<-unlist(outformres[2]);resultm<-matrix(unlist(outformres[1]),ncol=maxresm)
     stdmod<-noquote(matrix(sprintf(decimals,stdmod),nrow=(nump[1,i]-1)))
     colnames(stdmod)<-"coeff"
     rownames(stdmod)<-sdvlabs
     if (outscreen==1)
     {print(stdmod,right=T)}
    }
    if ((nms > 0) & (serial==0) & (sum(numint)==0) & ((normal==1) | (mc > 0)))
    {
     if (i < (nms+nys))
     {
      indcov[(((i-1)*nxvls)+1):(i*nxvls),(((i-1)*nxvls)+1):(i*nxvls)]<-varb[2:(1+nxvls),2:(1+nxvls)]
      mcsopath[(((i-1)*nxvls)+1):(i*nxvls),1]<-matrix(modrest9[2:(1+nxvls),1])
     }
     if (i==(nms+nys))
     {
      atm<-ncol(wherem)
      indcov[((nms*nxvls)+1):nrow(mcsopath),((nms*nxvls)+1):nrow(mcsopath)]<-varb[wherem[1,atm]:(wherem[1,atm]+nms-1),wherem[1,atm]:(wherem[1,atm]+nms-1)]
      mcsopath[((nms*nxvls)+1):nrow(mcsopath),1]<-matrix(modrest9[wherem[1,atm]:(wherem[1,atm]+nms-1),1])
      sobelok<-1          
     }
    }
    obsdirfx<-matrix(0,1,nxvls);dirzes<-matrix(0,1,nxvls)    
    if ((i ==(nms+nys)) & (bcmat[nrow(bcmat),1]==1))
    {
     direff<-matrix(direff[2:(1+nxvls),],nrow=nxvls)
     obsdirfx<-t(direff[,1])
     direfflb<-modresl
     direffl2<-vlabsm[2:(1+nxvls),]
     lmat<-matrix(0,nrow(b),1)
     lmat2<-matrix(1,nxvls,1)
     lmat[2:(1+nxvls),1]<-lmat2
     if (ydich != 1)
     {
      diromni<-process.ftest3(lmat,b,varb,1,brsq2,0,y,x)
     }
     if (ydich==1){diromni<-process.llrtest3(lmat,y,x,b,basemod,iterate,converge)}
    }

    if (numint[1,i] > 0)
    {
     if (outscreen==1)
     {
      cat("\nProduct terms key:\n")
      intkeym<-matrix(intkey[intstart:(intstart+numint[1,i]-1),],ncol=7)
      write.table(intkeym,quote=FALSE,row.names=FALSE,col.names=FALSE,sep = "  ")
     }
    }
    if (covcoeff==1)
    {
     if (outscreen==1){cat("\nCovariance matrix of regression parameter estimates:\n")}
     outformres<-process.outform3(varb,maxresm,resultm)
     maxresm<-unlist(outformres[2]);resultm<-matrix(unlist(outformres[1]),ncol=maxresm)
     varbpr<-noquote(matrix(sprintf(decimals,varb),nrow=nump[1,i]))
     rownames(varbpr)<-vlabsm;colnames(varbpr)<-vlabsm
     if (outscreen==1)
     {print(varbpr,right=T)}
    }
    #start X by M interaction */
    if ((model != 74) & (xmtest==1) & (nms > 0))
    {
     r2tmp<-brsq2;btmp<-b;varbtmp<-varb
     dfrestmp<-dfres
     tvaltmp<-tval
     xmtst<-matrix(0,nms,4)
     xmtstlbc=matrix(c(hcflab,"df1","df2","p"))
     if ((i==(nms+nys)) & (ydich==1))
     {xmtst<-matrix(0,nms,3);xmtstlbc<-c("Chi-sq","df","p")
     }
     xmtstlb<-" ";xmtmat<-x;numxint<-0
     if (i > 1)
     {
      for (xmints in (2:i))
      {
       x<-xmtmat
       if ((bcmat[(i+1),xmints]==1) & (wzcmat[(i+1),xmints] != 1)) 
       {
        if (bcmat[(i+1),1]==0)
        {
         x<-cbind(xmtmat,xtmp)
         if ((ydich==1) & (i==(nms+nys)))
         {
          LL2<-process.modelest(y,x,2,0,xp2,5,iterate,converge)
          basemodx<-LL2
         }
        }
        for (xmtlp1 in (1:nxvls))
        {
         mtmpmns<-sum(mtmp[,(xmints-1)])/nrow(mtmp)
         x<-as.matrix(cbind(x,(xtmp[,xmtlp1]*((mtmp[,(xmints-1)])-mtmpmns))))
        }
        if ((i < (nms+nys)) | (ydich==0))
        {
         modoutz<-process.modelest(y,x,1,1,xp2,hc)
         b<-matrix(unlist(modoutz[5]))
         varb<-matrix(unlist(modoutz[6]),nrow=nrow(b))
         lmat<-matrix(0,nrow(b),nxvls)
         lmattmp<-diag(nxvls)
         lmat[(nrow(lmat)-nxvls+1):nrow(lmat),]<-lmattmp
         fresult2<-process.ftest3(lmat,b,varb,0,0,1,y,x)
         numxint<-numxint+1
         xmtst[numxint,]<-fresult2
        }
        #right here
        if ((i==(nms+nys)) & (ydich==1))
        {
         LL2<-process.modelest(y,x,2,0,xp2,5,iterate,converge)
         chidfxm<-(basemodx-LL2)
         numxint<-numxint+1
         xmtst[numxint,1]<-chidfxm
         xmtst[numxint,3]<-(1-pchisq(chidfxm,df=nxvls))
        }
        xmtstlbt<-matrix(c(highlbx[(xmints-1),1]))
        xmtstlb<-rbind(xmtstlb,xmtstlbt)     
       }      
      }
     }
     x<-xmtmat
     if (numxint > 0)
     {           
      xmtstlb<-matrix(xmtstlb[(2:(numxint+1)),])
      xmtst<-matrix(xmtst[1:numxint,],nrow=numxint)
      if (nms==1){xmtstlb<-" "}
      outformres<-process.outform3(xmtst,maxresm,resultm)
      maxresm<-unlist(outformres[2]);resultm<-matrix(unlist(outformres[1]),ncol=maxresm)
      if ((i < (nms+nys)) | (ydich==0))
      {
       xmtst<-noquote(matrix(sprintf(decimals,xmtst),ncol=4))
       colnames(xmtst)<-xmtstlbc
       rownames(xmtst)<-xmtstlb
       if (outscreen==1)
       {cat("\nTest(s) of X by M interaction:\n")
       print(xmtst,right=T)}
      } 
      if ((i==(nms+nys)) & (ydich==1))
      {
       xmtst<-noquote(matrix(sprintf(decimals,xmtst),ncol=3))
       colnames(xmtst)<-xmtstlbc
       rownames(xmtst)<-xmtstlb
       if (outscreen==1)
       {cat("\nLikelihood ratio test(s) of X by M interaction:\n")
       print(xmtst,right=T)}
      } 
     }   
     b<-btmp;varb<-varbtmp;dfres<-dfrestmp;tval<-tvaltmp;brsq2<-r2tmp
    }
    #end X by M interaction

    #here is where we do F tests
    #START F
    if (criterr==0)
    {
     jj<-0
     for (j in (start:((start+i)-1)))
     {
      dbint<-0
      lmat<-whigh[1:nump[1,i],j]
      lmat2<-wzhigh[1:nump[1,i],j]
      if ((sum(lmat) > 0) & (sum(lmat2)==0)) 
      {         
       if ((i < (nms+nys)) | (ydich != 1))
       {
        fresult2<-process.ftest3(lmat,b,varb,chr=1,brsq2,0,y,x)
        lmatdb<-lmat
        dbint=dbint<-1
       }
       if ((ydich==1) & (i==(nms+nys)))
       {                      
        fresult2<-process.llrtest3(lmat,y,x,b,basemod,iterate,converge)
        lmatdb<-lmat
        dbint<-dbint+1
       }
       highf<-rbind(highf,fresult2)
       highf2<-rbind(highf2,fresult2)
       if (j==start){flabel<-matrix(c(flabel,"X*W"))}      
       if (j > start)
       {
        if (nms > 1){flabel<-matrix(c(flabel,highlbw[jj,1]))}
        if (nms==1)
        {
         if (xmint==0){flabel<-matrix(c(flabel,"M*W"))}
         if (xmint==1){flabel<-matrix(c(flabel,"X*M"))}       
        }
       }
      }     
      lmat<-zhigh[1:nump[1,i],j]
      lmat2<-wzhigh[1:nump[1,i],j]
      if ((sum(lmat) > 0) & (sum(lmat2)==0))
      {
       if ((i < (nms+nys)) | (ydich != 1))
       {
        fresult2<-process.ftest3(lmat,b,varb,chr=1,brsq2,0,y,x)
        dbint<-dbint+1             
       }
       if ((ydich==1) & (i==(nms+nys)))
       {
        fresult2<-process.llrtest3(lmat,y,x,b,basemod,iterate,converge)
        dbint<-dbint+1    
       }
       highf<-rbind(highf,fresult2)   
       highf2<-rbind(highf2,fresult2)                
       if (j==start){flabel<-matrix(c(flabel,"X*Z"))}
       if (j > start)
       {
        if (nms > 1){flabel<-matrix(c(flabel,highlbz[jj,1]))}
        if (nms==1){flabel<-matrix(c(flabel,"M*Z"))}
       }
      }
      if (dbint==2)
      {
       lmatdb<-(lmatdb+lmat)
       if ((ydich==1) & (i==(nms+nys)))
       {fresult2<-process.llrtest3(lmatdb,y,x,b,basemod,iterate,converge)}       
       if ((ydich != 1) | (i < (nms+nys)))
       {
        fresult2<-process.ftest3(lmatdb,b,varb,chr=1,brsq2,0,y,x)
       }
       dbint<-0 
       highf<-rbind(highf,fresult2)
       if ((jj==0) & (nms > 0)){flabel<-matrix(c(flabel,"BOTH(X)"))}  
       if ((jj==0) & (nms==0)){flabel<-matrix(c(flabel,"BOTH"))}  
       if ((jj > 0) & (nms==1)){flabel<-matrix(c(flabel,"BOTH(M)"))}  
       if ((nms > 1) & (jj > 0)){flabel<-matrix(c(flabel,highlbbt[jj,1]))}         
      } 
      lmat2<-wzhigh[1:nump[1,i],j]
      if (sum(lmat2) > 0)
      {
       if ((i < (nms+nys)) | (ydich != 1))
       {
        fresult2<-process.ftest3(lmat2,b,varb,chr=1,brsq2,0,y,x)
       }
       if ((ydich==1) & (i==(nms+nys)))
       {fresult2<-process.llrtest3(lmat2,y,x,b,basemod,iterate,converge)}
       highf<-rbind(highf,fresult2)
       highf2<-rbind(highf2,fresult2)
       if (j==start){flabel<-matrix(c(flabel,"X*W*Z"))}
       if (j > start)
       {
        if (nms > 1){flabel<-matrix(c(flabel,highlbwz[jj,1]))}
        if (nms==1){flabel<-matrix(c(flabel,"M*W*Z"))}
       }
      }
      jj<-jj+1
     }
     start<-start+i
     highfsz<-ncol(highf)
    }
    #END F

    if (nrow(highf) > 1)
    {
     highf<-matrix(highf[2:nrow(highf),],ncol=highfsz)
     highf2<-matrix(highf2[2:nrow(highf2),],ncol=highfsz)
     flabel<-as.matrix(flabel[2:nrow(flabel),])
     outformres<-process.outform3(highf,maxresm,resultm)
     maxresm<-unlist(outformres[2]);resultm<-matrix(unlist(outformres[1]),ncol=maxresm)
     highf<-noquote(matrix(sprintf(decimals,highf),ncol=highfsz))
     if (outscreen==1)
     {
      if ((i < (nms+nys)) | (ydich==0))
      {      
       cat("\nTest(s) of highest order unconditional interaction(s):\n")
       colnames(highf)<-matrix(c("R2-chng",hcflab,"df1","df2","p"))
       rownames(highf)<-flabel
       print(highf,right=T)      
      }
      if ((ydich==1) & (i==(nms+nys)))
      {
       cat("\nLikelihood ratio test of highest order\n")
       cat("unconditional interaction(s):\n")
       colnames(highf)<-matrix(c("Chi-sq", "df","p"))
       rownames(highf)<-flabel
       print(highf,right=T)
      }
     }      
     intpb<-as.matrix(highf2[,ncol(highf2)])
    }
    intstart<-intstart+numint[1,i]
   }
   #END E

   #Start PROBEandPLOT
   if (criterr==0)
   {
    threeway<-0;didprint<-0;didsome<-0;sigintct<-0
    #Start R
    for (jmed in (1:(nms+1)))
    {
     hasw<-0;hasz<-0;jnok<-0;nm1vls<-0;nm2vls<-0;panelgrp<-0
     focpred4<-matrix(" ")
     intprint<-0;modcat<-0    
     #Start A
     if (jmed <= i)
     {
      if ((jmed==1) & ((i+1)==nrow(bcmat)))
      {pathscnt<-pathscnt+1} else {
       paths<-cbind(paths,bcmat[(i+1),jmed])
       pathsw<-cbind(pathsw,wcmat[(i+1),jmed])
       pathsz<-cbind(pathsz,zcmat[(i+1),jmed])
       pathswz<-cbind(pathswz,wzcmat[(i+1),jmed])
       temp<-matrix(fochigh[,pathscnt]*bootloc[,i])
       pathsfoc<-as.matrix(cbind(pathsfoc,pathsfoc[,1]))
       if (jmed==1){pathtype<-cbind(pathtype,1)}
       if ((i+1)==nrow(bcmat)){pathtype<-cbind(pathtype,3)}
       if ((jmed > 1) & ((i+1) < nrow(bcmat))){pathtype<-cbind(pathtype,2)}
       if ((jmed==1) & (nxvls > 1) & (bcmat[(i+1),jmed]==1))
       {pathsfoc[,(pathscn2+1)]<-temp[2:(nxvls+1),1]}
       if ((jmed > 1) | ((jmed==1) & (nxvls==1)))
       {
        temp<-matrix(apply(temp,2,max))  #column max
        pathsfoc[1,(pathscn2+1)]<-temp
       }
       pathscnt<-pathscnt+1;pathscn2<-pathscn2+1
       if (i <= nms){pathsdv<-matrix(c(pathsdv,mnames[1,i]))}
       if (i > nms){pathsdv<-matrix(c(pathsdv,ynames))}
      }
      coeffcol<-coeffcol+1
      probettt<-matrix(coeffs[1:nrow(b),coeffcol])
      if ((jmed==1) & (bcmat[(i+1),jmed]==1))
      {
       omni<-matrix(0,nrow(probettt),nxvls)
       omnitmp<-diag(nxvls)
       omni[2:(1+nxvls),]<-omnitmp
      }
      if (sum(probettt) > 0)
      {
       probvarb<-matrix(999,sum(probettt),sum(probettt))
       probcoef<-matrix(999,sum(probettt),1)
       coefflp2<-1
       for (coefflp in (1:nrow(probettt)))
       {
        if (probettt[coefflp,1]==1)
        {
         probcoef[coefflp2,1]<-b[coefflp,1]
         coefflp2<-coefflp2+1
        }
       }
       coefflp<-0;coefflp2<-0
       for (iclp in (1:nrow(probettt)))
       {
        if (probettt[iclp,1]==1)
        {
         coefflp<-coefflp+1
         coefflp2<-coefflp
         probvarb[coefflp,coefflp]<-varb[iclp,iclp]
         if (iclp < nrow(probettt))
         {
          for (jclp in ((iclp+1):nrow(probettt)))
          {       
           if (probettt[jclp,1]==1)
           {
            coefflp2<-coefflp2+1
            probvarb[coefflp,coefflp2]<-varb[iclp, jclp]
            probvarb[coefflp2,coefflp]<-varb[iclp, jclp]
           }
          }
         }
        }
       } 
      } 
     }
     #End A
     xprobval<-as.matrix(xmodvals)
     if ((nxvls > 1) | (mcx > 0)){xprobval<-as.matrix(dummatx[,2:ncol(dummatx)])}
     #Start B
     if ((wcmat[(i+1),jmed]==1) & (zcmat[(i+1),jmed]==0))
     {
      numplps<-1;modvals<-wmodvals;probeval<-wmodvals;wheremv1<-wherexw
      nm1vls<-nwvls;lpstsp<-t(matrix(c(1,1)));modcat<-0;jnmod<-wtmp;jnmodlab<-wnames
      jnok<-1;jnmin<-wmin;jnmax<-wmax;wherejn1<-2
      if (jmed==1)
      {
       wherejn3<-wherexw[1,i]
       if (nxvls > 1){jnok<-0}
      }
      if (jmed > 1)
      {
       wherejn1<-wherem[(jmed-1),i]
       wherejn3<-wheremw[((2*jmed)-3),i]
      }
      if (nwvls > 1)
      {
       probeval<-wprobval
       lpstsp[1,2]<-ncol(probeval)
       modcat<-1;jnok<-0
      }
      if (wdich==1){modcat<-1;jnok<-0}
      problabs<-wnames
      focpred3<-t(matrix(c(wnames,"(W)"))) 
      if (xmint==1)
      {focpred3<-t(matrix(c(wnames,"(X)")))}
      hasw<-1
      modgrph<-wnames;intprint<-1;sigintct<-sigintct+1;
      printpbe<-intpb[sigintct,1]
     }
     #end B
     #start C
     if ((wcmat[(i+1),jmed]==0) & (zcmat[(i+1),jmed]==1))
     {
      numplps<-1;modvals<-zmodvals;probeval<-zmodvals;wheremv1<-wherexz
      nm1vls<-nzvls;lpstsp<-t(matrix(c(1,1)));jnok<-1;jnmod<-ztmp;jnmin<-zmin;jnmax<-zmax
      jnmodlab<-znames;wherejn1<-2
      if (jmed==1)
      { 
      wherejn3<-wherexz[1,i]
      if (nxvls > 1){jnok<-0}
      }
      if (jmed > 1){wherejn1<-wherem[(jmed-1),i];wherejn3<-wheremz[((2*jmed)-3),i]}
      if (nzvls > 1)
      {
       probeval<-zprobval;lpstsp[1,2]<-ncol(probeval);modcat<-1;jnok<-0
      }
      if (zdich == 1){modcat<-1;jnok<-0}
      problabs<-znames
      focpred3<-t(matrix(c(znames,"(Z)"))) 
      modgrph<-znames;hasz<-1;intprint<-1;sigintct<-sigintct+1;
      printpbe<-intpb[sigintct,1]
     }
     # end C
     # start D
     if ((wzcmat[(i+1),jmed]==1) |  ((wcmat[(i+1),jmed]==1) & (zcmat[(i+1),jmed]==1)))
     {
      numplps<-2;probecnt<-1;intprint<-1
      if (wzcmat[(i+1),jmed]==1)
      {
       sigintct<-sigintct+1;printpbe<-intpb[sigintct,1]
      } else {
       sigintct<-sigintct+2;printpbe<-min(intpb[(sigintct-1):sigintct,1])
      } 
      panelgrp<-1;hasw<-1;hasz<-1
      modgrph<-wnames
      lpstsp<-matrix(1,2,2)
      wheremv1<-wherexw;nm1vls<-nwvls;wheremv2<-wherexz;nm2vls<-nzvls;jnok<-0
      if (wzcmat[(i+1),jmed]==1){jnok<-1}
      if (jmed > 1){mprobval<-mmodvals}
      if (jmed==1){if (nxvls > 1){jnok<-0}}
      if (nwvls > 1){lpstsp[1,2]<-ncol(wprobval);modcat<-1;jnok<-0}
      if (zdich==1){modcat<-1;jnok<-0}
      lpstsp[2,1]<-lpstsp[1,2]+1;lpstsp[2,2]<-lpstsp[1,2]+1
      if (nzvls > 1)
      {
       lpstsp[2,1]<-lpstsp[1,2]+1
       lpstsp[2,2]<-lpstsp[1,2]+ncol(zprobval)
       jnok<-0
      }
      if (zdich==1){jnok<-0}
      omni3<-matrix(0,nrow(b),(nxvls*nwvls),0)
      if (jmed > 1){omni3<-matrix(0,nrow(b),nwvls)}
      focpred3<-t(matrix(c(wnames,"(W)")))
      if (xmint==1)
      {focpred3<-t(matrix(c(wnames,"(X)")))}
      focpred4<-t(matrix(c("      Moderator:", znames, "(Z)"))) 
      modvals<-matrix(0,(nrow(wmodvals)*nrow(zmodvals)),2)
      probeval<-matrix(0,(nrow(wmodvals)*nrow(zmodvals)),(ncol(wprobval)+ncol(zprobval)))
      for (probei in (1:nrow(wmodvals)))
      {
       for (probej in (1:nrow(zmodvals)))
       {
        modvals[probecnt,1]<-wmodvals[probei,1]
        probeval[probecnt,1:nwvls]<-wprobval[probei,]
        modvals[probecnt,2]<-zmodvals[probej,1]
        probeval[probecnt,(nwvls+1):(nwvls+nzvls)]<-zprobval[probej,]
        probecnt<-probecnt+1
       }
      }
      if (wzcmat[(i+1),jmed]==1)
      {
       numplps<-numplps+1
       probprod<-matrix(0,1,(ncol(wprobval)*ncol(zprobval)))
       lpstsp2<-t(matrix(c(1,1)))
       lpstsp=rbind(lpstsp,lpstsp2)
       lpstsp[3,1]<-lpstsp[2,2]+1
       lpstsp[3,2]<-lpstsp[2,2]+ncol(probprod)
       jnmod<-ztmp;jnmin<-zmin;jnmax<-zmax;jnmodlab<-znames
       if (jmed == 1){wherejn1<-wherexw[1,i];wherejn3<-wherexwz[1,i]}
       if (jmed > 1)
       {
        wherejn1<-wheremw[((2*jmed)-3),i]
        wherejn3<-wheremwz[((2*jmed)-3),i]
       }
       for (probei in (1:nrow(wmodvals)))
       {
        for (probej in (1:nrow(zmodvals)))
        {
         probtemp<-1
         for (probek in (1:ncol(wprobval)))
         {
          #probtemp<-cbind(probtemp,(wprobval[probei,probek]*zprobval[probej,]))
          probtemp<-cbind(probtemp,matrix((wprobval[probei,probek]*zprobval[probej,]),ncol=ncol(zprobval)))
         }
          probprod<-rbind(probprod,probtemp[1,2:ncol(probtemp)])
        }
       }
       probprod<-probprod[2:nrow(probprod),]
       probeval<-cbind(probeval,probprod)
      }
      problabs<-cbind(wnames,znames)
     }
     # end D
     # start E
     if (intprint==1)
     {
      focpred<-"Focal predictor:"
      if (jmed==1)
      {focpred<-cbind(focpred,xnames,"(X)");focplotv<-as.matrix(xmodvals)}
      if (jmed >1)
      {
       if (nms > 1){focpred<-cbind(focpred,mnames[1,(jmed-1)], medlb2[(jmed-1),1])}
       if (nms==1){focpred<-cbind(focpred,mnames[1,(jmed-1)], "(M)")}
       focplotv<-as.matrix(mmodvals[,(jmed-1)],nrow=nrow(mmodvals))
      }
      focpred2<-cbind("      Moderator:",focpred3)
      focpred<-rbind(focpred,focpred2)
      if (ncol(focpred4) > 1){focpred<-rbind(focpred,focpred4);focpred4<-matrix(c(" "))}
      if (outscreen==1)
      {
       if (((plot==1) | (plot==2)) | (printpbe <= intprobe))      
       {cat("----------\n");write.table(focpred,quote=FALSE,row.names=FALSE,col.names=FALSE)}
      }
      foctmp<-matrix(1,nrow(modvals),1)
      probexpl<-1
      probeva2<-cbind(foctmp,probeval)
      if ((jmed==1) & (nxs > 0) & (mcx > 0)){probexpl<-nxvls}
      foctmp<-matrix(1,nrow(modvals),1)
      modvals3<-matrix(0,1,(6+ncol(problabs)))
      probrown<-matrix(0,nrow(probeval),1)
      jtmp<-1
      for (probei in (1:nrow(probeval))){probrown[probei,1]<-jtmp;jtmp<-jtmp+nxvls}
      probrow<-999;modvarl<-problabs     

      # start F */ 
      if ((plot==1) | (plot==2) | (nxvls > 1))
      {
       plotvals<-matrix(999,(nrow(modvals)*nrow(focplotv)),(ncol(modvals)+1))
       for (ploti in (1:nrow(modvals)))
       {
        for (plotj in (1:nrow(focplotv)))
        {
         plotvals[(((ploti-1)*nrow(focplotv))+plotj),2:ncol(plotvals)]<-modvals[ploti,]
         plotvals[(((ploti-1)*nrow(focplotv))+plotj),1]<-focplotv[plotj,1]
        }
       }
       focpredn<-3
       if (jmed==1)
       {
        if (nxvls > 1){focpredn<-(nxvls+1)}
        if ((nxvls==1) & (xdich==1)){focpredn<-2}
       }
       meanmat<-diag(c(means))
       onesmat<-matrix(1,nrow(meanmat),(nrow(probeval)*focpredn))
       probeplt<-t(diag(means)%*%onesmat)
       # start G
       if (jmed==1)
       {
        if ((wcmat[(i+1),1]==1) | (zcmat[(i+1),1]==1))
        {
         plotcnt<-1;iloops<-nwpval*nzpval;plotmx<-nxpval*nzpval
         if ((wcmat[(i+1),1]==1) & (zcmat[(i+1),1]==0))
         {iloops<-nwpval;plotmx<-nxpval}
         if ((wcmat[(i+1),1]==0) & (zcmat[(i+1),1]==1))
         {iloops<-nzpval;plotmx<-nxpval}     
         xestvals<-matrix(-999,(nxpval*iloops),ncol(xprobval))
         if (wcmat[(i+1),1]==1){westvals<-matrix(-999,nrow(xestvals),ncol(wprobval))} 
         if (zcmat[(i+1),1]==1){zestvals<-matrix(-999,nrow(xestvals),ncol(zprobval))}
         for (ploti in (1:iloops))
         {
          for (plotj in (1:nxpval))
          {xestvals[plotcnt,]<-xprobval[plotj,];plotcnt<-plotcnt+1}
         }
         plotcnt<-1;plotcnt1<-1;plotcnt2<-1;plotcntz<-1
         for (ploti in (1:(iloops*nxpval)))
         {
          if (wcmat[(i+1),1]==1){westvals[ploti,]=wprobval[plotcnt1,]}        
          if ((wcmat[(i+1),1]==0) & (zcmat[(i+1),1]==1))
          {zestvals[ploti,]<-zprobval[plotcnt1,]}   
          if ((wcmat[(i+1),1]==1) & (zcmat[(i+1),1]==1))
          {zestvals[ploti,]<-zprobval[plotcnt2,];plotcntz=plotcntz+1}
          plotcnt<-plotcnt+1
          if (plotcnt > plotmx){plotcnt<-1;plotcnt1<-plotcnt1+1}
          if (plotcntz > nxpval)
          {
           plotcnt2<-plotcnt2+1;plotcntz<-1
           if (plotcnt2 > nzpval){plotcnt2<-1} 
          }
         }
         probeplt[,2:(1+(ncol(xestvals)))]<-xestvals 
         if (wcmat[(i+1),1]==1)
         {probeplt[,wherew[1,i]:wherew[2,i]]<-westvals}            
         if (zcmat[(i+1),1]==1)
         {probeplt[,wherez[1,i]:wherez[2,i]]<-zestvals}  
        }
       }
       # end G
       # start H
       if (jmed > 1)
       {
        if ((wcmat[(i+1),jmed]==1) | (zcmat[(i+1),jmed]==1))
        {
         plotcnt<-1;iloops<-(nwpval*nzpval);plotmx<-(3*nzpval)
         if ((wcmat[(i+1),jmed]==1) & (zcmat[(i+1),jmed]==0))
         {iloops<-nwpval;plotmx<-3}
         if ((wcmat[(i+1),jmed]==0) & (zcmat[(i+1),jmed]==1))
         {iloops<-nzpval;plotmx<-3}
         mestvals<-matrix(-999,(3*iloops),1)
         if (wcmat[(i+1),jmed]==1)
         {westvals<-matrix(-999,nrow(mestvals),ncol(wprobval))}
         if (zcmat[(i+1),jmed]==1)
         {zestvals<-matrix(-999,nrow(mestvals),ncol(zprobval))}
         for (ploti in (1:iloops))
         {
          for (plotj in (1:3))
          {
           mestvals[plotcnt,]<-mprobval[plotj,(jmed-1)]
           plotcnt<-plotcnt+1
          }
         }
         plotcnt<-1;plotcnt1<-1;plotcnt2<-1;plotcntz<-1
         for (ploti in (1:(iloops*3)))
         {
          if (wcmat[(i+1),jmed]==1)
          {westvals[ploti,]<-wprobval[plotcnt1,]}     
          if ((wcmat[(i+1),jmed]==0) & (zcmat[(i+1),jmed]==1))
          {zestvals[ploti,]<-zprobval[plotcnt1,]}   
          if ((wcmat[(i+1),jmed]==1) & (zcmat[(i+1),jmed]==1))
          {zestvals[ploti,]<-zprobval[plotcnt2,];plotcntz<-plotcntz+1}
          plotcnt<-plotcnt+1
          if (plotcnt > plotmx){plotcnt<-1;plotcnt1<-plotcnt1+1}        
          if (plotcntz > 3)
          {
           plotcnt2<-plotcnt2+1;plotcntz<-1
           if (plotcnt2 > nzpval){plotcnt2<-1} 
          }
         }
         probeplt[,wherem[(jmed-1),i]]<-mestvals
         if (wcmat[(i+1),jmed]==1)
         {
          if (model != 74)
          {probeplt[,(wherew[1,i]):(wherew[2,i])]<-westvals}
          if (model==74)
          {probeplt[,(wherex[1,i]):(wherex[2,i])]<-westvals}
         }    
         if (zcmat[(i+1),jmed]==1)
         {probeplt[,(wherez[1,i]):(wherez[2,i])]<-zestvals}  
        }
       } 
       # end H
       # Here I am doing the multiplications to produce data for the plot
       prodloop <-1
       if (jmed==1){prodloop<-ncol(xestvals)}
       if ((wcmat[(i+1),jmed])==1)
       {
        plotcnt<-0
        for (ploti in (1:prodloop))
        {
         for (plotj in (1:ncol(westvals)))
         {
          if (jmed==1)
          {probeplt[,(wherexw[1,i]+plotcnt)]<-(xestvals[,ploti]*westvals[,plotj])}
          if (jmed > 1)
          {probeplt[,(wheremw[((jmed*2)-3) ,i]+plotcnt)]<-(mestvals[,ploti]*westvals[,plotj])}
          plotcnt<-plotcnt+1
         }
        } 
       }
       if ((zcmat[(i+1),jmed])==1)
       {
        plotcnt<-0
        for (ploti in (1:prodloop))
        {
         for (plotj in (1:ncol(zestvals)))
         {   
          if (jmed==1)
          {probeplt[,(wherexz[1,i]+plotcnt)]<-(xestvals[,ploti]*zestvals[,plotj])}    
          if (jmed > 1)
          {probeplt[,(wheremz[((jmed*2)-3),i]+plotcnt)]<-(mestvals[,ploti]*zestvals[,plotj])}
          plotcnt<-plotcnt+1
         }
        } 
       }
       if ((wzcmat[(i+1),jmed])==1)
       {
        plotcnt<-0;threeway<-1
        for (ploti in (1:ncol(westvals)))
        {
         for (plotj in (1:ncol(zestvals)))
         {
          probeplt[,(wherewz[1,i]+plotcnt)]<-(westvals[,ploti]*zestvals[,plotj])
          plotcnt<-plotcnt+1
         }
        }  
        plotcnt<-0
        for (plotk in (1:prodloop))
        {
         for (ploti in (1:ncol(westvals)))
         {
          for (plotj in (1:ncol(zestvals)))
          {
           if (jmed==1)
           {probeplt[,(wherexwz[1,i]+plotcnt)]<-(xestvals[,plotk]*westvals[,ploti]*zestvals[,plotj])}          
           if (jmed > 1)
           {probeplt[,(wheremwz[((jmed*2)-3),i]+plotcnt)]<-(mestvals[,plotk]*westvals[,ploti]*zestvals[,plotj])}          
           plotcnt<-plotcnt+1
          }
         } 
        }
       }
       # here is where we add holding constant products as needed
       # start I
       for (newplp in (1:i))
       {  
        if (newplp != jmed)
        {  
         if (wcmat[(i+1),newplp]==1)
         {
          prodloop<-1
          if (newplp==1){prodloop<-nxvls}
          plotcnt<-0
          for (ploti in (1:prodloop))
          {
           for (plotj in (1:nwvls))
           {
            if (newplp==1)
            {probeplt[,(wherexw[1,i]+plotcnt)]<-probeplt[,(1+ploti)]*probeplt[,(wherew[1,i]+plotj-1)]}
            if (newplp > 1)
            {
             if (model != 74)
             {probeplt[,(wheremw[((newplp*2)-3) ,i]+plotcnt)]<-probeplt[,wherem[(newplp-1),i]]*probeplt[,(wherew[1,i]+plotj-1)]}
             if (model==74)
             {probeplt[,(wheremw[((newplp*2)-3) ,i]+plotcnt)]<-probeplt[,wherem[(newplp-1),i]]*probeplt[,(wherex[1,i]+plotj-1)]}           
            }
            plotcnt<-plotcnt+1
           }
          } 
         }  
         if (zcmat[(i+1),newplp]==1)
         {
          prodloop<-1
          if (newplp==1){prodloop<-nxvls}
          plotcnt<-0
          for (ploti in (1:prodloop))
          {
           for (plotj in (1:nzvls))
           {
            if (newplp==1)
            {probeplt[,(wherexz[1,i]+plotcnt)]<-probeplt[,(1+ploti)]*probeplt[,(wherez[1,i]+plotj-1)]}
            if (newplp > 1)
            {probeplt[,(wheremz[((newplp*2)-3),i]+plotcnt)]<-probeplt[,wherem[(newplp-1),i]]*probeplt[,(wherez[1,i]+plotj-1)]}
            plotcnt<-plotcnt+1
           }
          } 
         }
         if (wzcmat[(i+1),newplp]==1)
         {
          plotcnt<-0
          if (threeway==0)
          {
           for (ploti in (1:nwvls))
           {
            for (plotj in (1:nzvls))
            {
             probeplt[,(wherewz[1,i]+plotcnt)]<-probeplt[,(wherew[1,i]+ploti-1)]*probeplt[,(wherez[1,i]+plotj-1)]
             plotcnt<-plotcnt+1
            }
           }
          } 
          prodloop<-1
          if (newplp==1){prodloop<-nxvls}
          plotcnt<-0
          for (plotk in (1:prodloop))
          {
           for (ploti in (1:nwvls))
           {
            for (plotj in (1:nzvls))
            {
             if (newplp==1)
             {probeplt[,(wherexwz[1,i]+plotcnt)]<-probeplt[,(1+plotk)]*probeplt[,(wherew[1,i]+ploti-1)]*probeplt[,(wherez[1,i]+plotj-1)]}      
             if (newplp > 1)
             {probeplt[,(wheremwz[((newplp*2)-3),i]+plotcnt)]<-probeplt[,wherem[(newplp-1),i]]*probeplt[,(wherew[1,i]+ploti-1)]*probeplt[,(wherez[1,i]+plotj-1)]}      
             plotcnt<-plotcnt+1
            }
           } 
          }
         }      
        }
       }
       #END I
       predvals<-probeplt%*%b
          
       if ((i==(nms+nys)) & (ydich==1))
       {
        predvalt<-as.numeric(predvals < 709.7)
        prevalt7<-(1-predvalt)*(709.7)
        predvals<-(predvals*predvalt)+prevalt7
        expyhat<-exp(predvals)/(1+exp(predvals))
       } 
       sepred<-matrix(999,nrow(plotvals),3)
       for (sei in (1:nrow(plotvals)))
       {
        ask<-as.matrix(probeplt[sei,],ncol=ncol(probeplt))       
        sepred[sei,1]<-sqrt(t(ask)%*%varb%*%ask)
        if ((i < (nms+nys)) | (ydich==0))
        {
         sepred[sei,2]<-predvals[sei,1]-tval*sepred[sei,1]
         sepred[sei,3]<-predvals[sei,1]+tval*sepred[sei,1]
        }
        if ((i==(nms+nys)) & (ydich==1))
        {
         sepred[sei,2]<-predvals[sei,1]-xp2*sepred[sei,1]
         sepred[sei,3]<-predvals[sei,1]+xp2*sepred[sei,1]
        }
       }
       prevloc<-ncol(plotvals)+1
       probeplt<-cbind(plotvals,predvals)
       if (plot==2){probeplt<-cbind(probeplt,sepred)}
       if ((i==(nms+nys)) & (ydich==1))
       {probeplt<-cbind(probeplt,expyhat)}
       didsome<-0
     }
     # END F

     # here is the loop that is printing the conditional effects
     # this does conditional two way interactions
     # START J
     if ((wzcmat[(i+1),jmed]==1) & (printpbe <= intprobe))
     {
      if (jmed==1)
      {omnilp2<-nxvls*nwvls;omnitmp<-diag(omnilp2);omni3[wherexw[1,i]:wherexw[2,i],]<-omnitmp}  
      if (jmed>1)
      {omnilp2<-nwvls;omnitmp<-diag(omnilp2);omni3[wheremw[((jmed*2)-3),i]:wheremw[((jmed*2)-2),i],]<-omnitmp}
      omnif<-matrix(0,1,4)
      if ((i==(nms+nys)) & (ydich==1))
      {omnif<-matrix(0,1,3)}
      condeff3<-0
      for (omnilp1 in (1:nrow(zprobval)))
      {
       for (omnilp in (1:omnilp2))
       {
        if (jmed==1)
        {
         omni3[(wherexwz[1,i]+((omnilp-1)*nzvls)):(wherexwz[1,i]+((omnilp-1)*nzvls)+(nzvls-1)),omnilp]<-t(zprobval[omnilp1,])
        }
        if (jmed > 1)
        {
         omni3[(wheremwz[((jmed*2)-3),i]+((omnilp-1)*nzvls)):(wheremwz[((jmed*2)-3),i]+((omnilp-1)*nzvls)+(nzvls-1)),omnilp]<-t(zprobval[omnilp1,])
        }
       }
       condeff<-t(omni3)%*%b
       condeff3<-rbind(condeff3,condeff)
       fresult2<-process.ftest3(omni3,b,varb,chr=0,brsq2,1,y,x)

       if ((i==(nms+nys)) & (ydich==1))
       {
        fratio<-fresult2[1,1]*ncol(omni3)
        pfr<-(1-pchisq(fratio,df=ncol(omni3)))
        fresult2<-cbind(fratio,ncol(omni3),pfr)
       }
       omnif<-rbind(omnif,fresult2)
      }
      omnif<-matrix(omnif[2:nrow(omnif),],ncol=ncol(omnif))
      clabtmp<-znames
      condeff3<-matrix(condeff3[2:nrow(condeff3),],ncol=ncol(condeff3))
      if ((nxvls*nwvls)==1)
      {
       omnif<-cbind(condeff3,omnif)
       clabtmp<-cbind(clabtmp,"effect")
      } 
      omnif<-cbind(zmodvals,omnif)
      if ((i < nms+nys) | (ydich==0))
      {clabtmp<-c(clabtmp,hcflab,"df1","df2","p")}      
      if ((i==(nms+nys)) & (ydich==1))
      {clabtmp<-c(clabtmp,"Chi-sq","df","p")}
      outformres<-process.outform3(omnif,maxresm,resultm)
      maxresm<-unlist(outformres[2]);resultm<-matrix(unlist(outformres[1]),ncol=maxresm)
      omnif2<-noquote(matrix(sprintf(decimals,omnif),nrow=nrow(omnif)))
      colnames(omnif2)<-clabtmp
      rownames(omnif2)<-t(matrix(replicate(nrow(omnif)," ")))
      if (outscreen==1)
      {
       if (jmed==1)
       {cat("\nTest of conditional X*W interaction at value(s) of Z:\n")
       print(omnif2,right=T)}
       if (jmed > 1)
       {cat("\nTest of conditional M*W interaction at value(s) of Z:\n")
       print(omnif2,right=T)}
      }
     }
     # END J

      # start O 
      for (probei in (1:probexpl))
      {
       if (probexpl > 1)
       {
        foctmp<-matrix(0,nrow(modvals),probexpl)
        foctmp[,probei]<-foctmp[,probei]+1
        probtemp<-matrix(0,nrow(modvals),1)
        for (probem in (1:numplps))
        {for (probek in (1:nxvls))
         {for (probej in (lpstsp[probem,1]:lpstsp[probem,2]))
          {probtemp<-cbind(probtemp,foctmp[,probek]*probeval[,probej])}  
         }
        }
        probeva2<-probtemp[,2:ncol(probtemp)]
        probeva2<-cbind(foctmp,probeva2)
       }    
       probres<-probeva2%*%probcoef
       probrese<-as.matrix(sqrt(diag(probeva2%*%probvarb%*%t(probeva2)))) 
       tratio<-probres/probrese
       if ((ydich==1) & (i==(nms+nys)))
       {
        p<-2*(1-pnorm(abs(tratio)))
       } else {
        p<-2*pt(-abs(tratio),df=dfres)
       }
       modvals2<-cbind(modvals,probres,probrese,tratio, p)
       if ((i < nms+nys) | (ydich==0))
       {
        modvals2<-cbind(modvals2,(probres-as.numeric(tval)*probrese),(probres+as.numeric(tval)*probrese))   
        problabs<-cbind(problabs,"effect",hclab,"t", "p", "LLCI", "ULCI")
       }
       if ((ydich==1) & (i==(nms+nys)))
       {
        modvals2<-cbind(modvals2,(probres-xp2*probrese),(probres+xp2*probrese))
        problabs<-cbind(problabs,"effect","se","Z", "p", "LLCI", "ULCI")
       }
       # start L
       if ((probexpl > 1) & (printpbe <= intprobe))
       {
        if (hasz==1){printz<-1}
        if (hasw==1){printw<-1}
        probrlab<-matrix(xcatlab[probei,1],nrow(modvals),1)
        modvals3<-rbind(modvals3,modvals2)
        probrow<-rbind(probrow,probrown)
        probrown<-probrown+1
        if (probei==probexpl)
        {
         xproblab<-xcatlab[1:nxvls,1]
         probrow<-matrix(probrow[2:nrow(probrow),1])
         modvals3<-modvals3[2:nrow(modvals3),]
         #modvals3<-modvals3[order(modvals3[,1]),]
         modvals3<-modvals3[order(probrow[,1]),]
         start2<-1
         problabs<-problabs[1,(1+(ncol(modvarl))):ncol(problabs)]
         pstart<-1
         # start K
         for (probek in (1:nrow(probeval)))
         {
          endstart<-start2+(nxvls-1)
          temp<-modvals3[start2:endstart,(1+ncol(modvarl)):ncol(modvals3)]
          temp2<-t(modvals3[start2:start2,1:ncol(modvarl)])
          trnames<-t(modvarl)
          if (outscreen==1)
          {
           if (probek > 1){cat("----------\n")}
           if (probek==1)
           {
            cat("\nConditional effects of the focal predictor at values of the moderator(s):\n")
            if ((jmed==1) & (i ==(nms+nys)) & (nms > 0))
            {
             if (nxvls==1)
             {cat("\n(These are also the conditional direct effects of X on Y.)\n")}
             if (nxvls != 1)
             {cat("\n(These are also the relative conditional direct effects of X on Y.)\n")}
            }
            cat("\n")
           }
           cat("Moderator value(s):\n")
          }
          temp22<-matrix(temp2)
          outformres<-process.outform3(temp22,maxresm,resultm,1)
          maxresm<-unlist(outformres[2]);resultm<-matrix(unlist(outformres[1]),ncol=maxresm)
          temp2<-noquote(matrix(sprintf(decimals,temp2),nrow=nrow(temp2)))
          temp3<-matrix(temp2[1,1:ncol(temp2)])
	    rownames(temp3)<-trnames
          colnames(temp3)<-" "
          if (outscreen==1)
          {print(temp3,right=TRUE,quote=FALSE)
           cat("\n")}
          #for (mdpntr in (1:ncol(temp2)))
          #{
          #write.table(temp2[1,mdpntr],quote=FALSE,row.names=trnames[mdpntr,1],col.names=FALSE)
          #}          
          outformres<-process.outform3(temp,maxresm,resultm)
          maxresm<-unlist(outformres[2]);resultm<-matrix(unlist(outformres[1]),ncol=maxresm)
          temp<-noquote(matrix(sprintf(decimals,temp),nrow=nrow(temp)))
          colnames(temp)<-problabs[1:ncol(temp)]
          rownames(temp)<-xproblab
          if (outscreen==1)
          {print(temp,right=T)}
          start2<-start2+nxvls
          didsome<-1
          if (jmed==1)
          {
           mod1val<-probeval[probek,1:nm1vls]
           for (omnilp in (1:nxvls))
           {
            omni[(wheremv1[1,i]+((omnilp-1)*nm1vls)):(wheremv1[1,i]+((omnilp-1)*nm1vls)+(nm1vls-1)),omnilp]<-t(mod1val)
            if (nm1vls < ncol(probeval))
            {
             mod2val<-probeval[probek,(nm1vls+1):(nm1vls+nm2vls)]
             omni[(wheremv2[1,i]+((omnilp-1)*nm2vls)):(wheremv2[1,i]+((omnilp-1)*nm2vls)+(nm2vls-1)),omnilp]<-t(mod2val)
             if ((nm1vls+nm2vls) < ncol(probeval))
             {
              intlen<-(nm1vls*nm2vls)
              modintvl<-probeval[probek,(nm1vls+nm2vls+1):ncol(probeval)]
              omni[(wherexwz[1,i]+((omnilp-1)*intlen)):(wherexwz[1,i]+((omnilp-1)*intlen)+(intlen-1)),omnilp]<-t(modintvl)
             }
            }
           }
           fresult2<-process.ftest3(omni,b,varb,0,brsq2,1,y,x)
           if ((i == (nms+nys)) & (ydich==1))
           {
            fratio<-fresult2[1,1]*nxvls
            pfr<-(1-pchisq(fratio,df=nxvls))
            fresult2<-cbind(fratio,nxvls,pfr)
           }
           outformres<-process.outform3(fresult2,maxresm,resultm)
           maxresm<-unlist(outformres[2]);resultm<-matrix(unlist(outformres[1]),ncol=maxresm)
           fresult2<-noquote(matrix(sprintf(decimals,fresult2),nrow=nrow(fresult2)))
           if (outscreen==1)
           {
            if (i < (nms + nys) | (ydich == 0))
            {
             cat("\nTest of equality of conditional means\n")            
             colnames(fresult2)<-c(hcflab,"df1","df2","p")
             rownames(fresult2)<-" "
             print(fresult2,right=T)
            }
            if ((i==(nms + nys)) & (ydich==1))
            {
             cat("\nTest of equality of conditional logits or probabilities\n")
             rownames(fresult2)<-" "
             colnames(fresult2)<-c("Chi-sq","df","p")
             print(fresult2,right=T)            
            }
           }
           probetmp<-probeplt[pstart:(pstart+nxvls),1]
           probetmp<-cbind(probetmp,probeplt[pstart:(pstart+nxvls),prevloc:ncol(probeplt)])
           pstart<-pstart+(nxvls+1)
           outformres<-process.outform3(probetmp,maxresm,resultm)
           maxresm<-unlist(outformres[2]);resultm<-matrix(unlist(outformres[1]),ncol=maxresm)
           probetmp<-noquote(matrix(sprintf(decimals,probetmp),nrow=nrow(probetmp)))
           if (outscreen==1)
           {
            if (i < ((nms + nys)) | (ydich==0))
            {   
             clabtmp<-cbind(xnames, outnames[i,1], hclab, "LLCI", "ULCI")
             cat("\nEstimated conditional means being compared:\n")
             colnames(probetmp)<-clabtmp[1:ncol(probetmp)]
             rownames(probetmp)<-t(matrix(replicate(nrow(probetmp)," ")))
             print(probetmp,right=T)
            }
            if ((i==(nms+nys)) & (ydich==1))
            {
             cat("\nEstimated conditional logits and probabilities:\n")
             clabtmp<-c(xnames, outnames[i,1], "prob")
             probetm2<-matrix(probetmp[,1:2],ncol=2)
             probetm2<-cbind(probetm2,probetmp[,ncol(probetmp)])
             probetm2<-noquote(probetm2)
             colnames(probetm2)<-clabtmp[1:ncol(probetm2)]
             rownames(probetm2)<-t(matrix(replicate(nrow(probetm2)," ")))
             print(probetm2,right=T)
            }
           }
          }
         }
         # end K
        }
       }
       # end L
       # start N
       if ((probexpl==1) & (printpbe <= intprobe))
       {
        outformres<-process.outform3(modvals2,maxresm,resultm)
        maxresm<-unlist(outformres[2]);resultm<-matrix(unlist(outformres[1]),ncol=maxresm)
        modva2<-noquote(matrix(sprintf(decimals,modvals2),nrow=nrow(modvals2)))
        colnames(modva2)<-problabs[1:ncol(modva2)]
        rownames(modva2)<-t(matrix(replicate(nrow(modva2)," ")))
        if (outscreen==1)
        {cat("\nConditional effects of the focal predictor at values of the moderator(s):\n")
        print(modva2,right=T)}
        didsome<-1
        if (hasz==1){printz<-1}
        if (hasw==1){printw<-1}
        # start M (JN method)
        if ((jn==1) & (jnok==1))
        {
         if (criterr==0)
         {
          dfres<-(n-nrow(b));
          roots<-as.matrix(99999)
          jncrit<-(dfres* (exp((dfres-(5/6))*((xp2/(dfres-(2/3)+(.11/dfres)))*(xp2/(dfres-(2/3)+(.11/dfres)))))-1))
          if ((i ==(nms+nys)) & (ydich==1)){jncrit<-xp2*xp2}
          jnb1<-b[wherejn1,1]
          jnb3<-b[wherejn3,1]
          jnsb1<-varb[wherejn1,wherejn1]
          jnsb3<-varb[wherejn3,wherejn3]
          jnsb1b3<-varb[wherejn1,wherejn3]
          ajn<-(jncrit*jnsb3)-(jnb3*jnb3)
          bjn<-2*((jncrit*jnsb1b3)-(jnb1*jnb3))
          cjn<-((jncrit*jnsb1)-(jnb1*jnb1))
          radarg<-(bjn*bjn)-(4*ajn*cjn)
          den<-2*ajn
          nrts<-0
          if ((radarg >= 0) & (den != 0))
          {
           x21<-(-bjn+sqrt(radarg))/den
           x22<-(-bjn-sqrt(radarg))/den
           if ((x21 >= jnmin) & (x21 <= jnmax))
           {nrts<-1;roots<-rbind(roots,x21)}
           if ((x22 >= jnmin) & (x22 <= jnmax))
           {nrts<-nrts+1;roots<-rbind(roots,x22)}
           roots<-cbind(roots,matrix(0,nrow(roots),2))
          }
          if (nrts > 0)
	    {	                          
           roots<-matrix(roots[2:nrow(roots),1:3],ncol=3)
           roots[1,2]<-sum((as.numeric(jnmod < roots[1,1]))/n)*100
           roots[1,3]<-sum((as.numeric(jnmod > roots[1,1]))/n)*100
           if (nrow(roots)==2)
           {
            roots[2,2]<-sum((as.numeric(jnmod < roots[2,1]))/n)*100
            roots[2,3]<-sum((as.numeric(jnmod > roots[2,1]))/n)*100
           }
           roots2<-noquote(matrix(sprintf(decimals,roots),nrow=nrow(roots)))
           colnames(roots2)<-c("Value", "% below", "% above")
           rownames(roots2)<-t(matrix(replicate(nrow(roots)," ")))
           if (nrts==1){tmprts=matrix(99999,1,3);roots<-rbind(roots,tmprts)}
           if (outscreen==1)
           {cat("\nModerator value(s) defining Johnson-Neyman significance region(s):\n")
           print(roots2,right=T)}
          }
          if (nrts==0)
          {
           roots<-as.matrix(c(99999,99999))
           if (outscreen==1)
           {cat("\nThere are no statistical significance transition points within the observed\n")
           cat("range of the moderator found using the Johnson-Neyman method.\n")}
          }
          outformres<-process.outform3(roots,maxresm,resultm,1)
          maxresm<-unlist(outformres[2]);resultm<-matrix(unlist(outformres[1]),ncol=maxresm)
          jnvals<-matrix(0,23,7)
          for (jni in (0:(21-nrts)))
          {jnvals[(jni+1),1]<-jnmin+(jni*((jnmax-jnmin)/(21-nrts)))}
          if (nrts > 0)
          {
           for (jni in (1:nrts))
           {
            for (jnj in (2:nrow(jnvals)))
            {
             if ((roots[jni,1] > jnvals[(jnj-1),1]) & (roots[jni,1] < jnvals[jnj,1]))
             {
              jnvals[(jnj+1):(21+jni),1]<-jnvals[jnj:(20+jni),1]
              jnvals[jnj,1]<-roots[jni,1]
             }
            }
           }
          }
          jnvals<-jnvals[1:22,] 
          for (jni in (1:nrow(jnvals)))
          {
           jnvals[jni,2]<-jnb1+jnb3*jnvals[jni,1]
           jnvals[jni,3]<-sqrt(jnsb1+2*jnvals[jni,1]*jnsb1b3+(jnvals[jni,1]*jnvals[jni,1])*jnsb3)
           jnvals[jni,4]<-jnvals[jni,2]/jnvals[jni,3]
           jnvals[jni,5]<-2*pt(-abs(jnvals[jni,4]), df=dfres)
           jnvals[jni,6]<-jnvals[jni,2]-sqrt(jncrit)*jnvals[jni,3]
           jnvals[jni,7]<-jnvals[jni,2]+sqrt(jncrit)*jnvals[jni,3]
           if ((i==(nms + nys)) & (ydich==1))
           {
            jnvals[jni,5]<-2*(1-pnorm(abs(jnvals[jni,4])))
            jnvals[jni,6]<-jnvals[jni,2]-xp2*jnvals[jni,3]
            jnvals[jni,7]<-jnvals[jni,2]+xp2*jnvals[jni,3]
           }
          } 
          outformres<-process.outform3(jnvals,maxresm,resultm)
          maxresm<-unlist(outformres[2]);resultm<-matrix(unlist(outformres[1]),ncol=maxresm)
          jnvals2<-noquote(matrix(sprintf(decimals,jnvals),nrow=nrow(jnvals)))
          rownames(jnvals2)<-t(matrix(replicate(nrow(jnvals)," ")))
          if ((i < nms+nys) | (ydich==0))
          {colnames(jnvals2)<-c(jnmodlab,"effect", hclab, "t", "p", "LLCI", "ULCI")}          
          if ((i==(nms+nys)) & (ydich==1))
          {colnames(jnvals2)<-c(jnmodlab,"effect","se","Z", "p", "LLCI", "ULCI")}
          if (outscreen==1)
          {
           if (((wcmat[(i+1),jmed]==1) | (zcmat[(i+1),jmed]==1)) & (wzcmat[(i+1),jmed]==0))
           {cat("\nConditional effect of focal predictor at values of the moderator:\n");print(jnvals2,right=T)}          
           if ((jmed==1) & (wzcmat[(i+1),jmed]==1))
           {cat("\nConditional X*W interaction at values of the moderator Z:\n");print(jnvals2,right=T)}   
           if ((jmed > 1) & (wzcmat[(i+1),jmed]==1))
           {cat("\nConditional M*W interaction at values of the moderator Z:\n");print(jnvals2,right=T)} 
          }  
         }
        }
        # END M (JN method)
       }
       if ((i==(nms+nys)) & (jmed==1) & (bcmat[nrow(bcmat),1]==1))
       {
        if (probei==1)
        {direfflb<-problabs;direff<-modvals2}
        if (probei>1)
        {direff<-rbind(direff,modvals2)}  
       }
       intprint<-0
    #  This does the contrast for conditional effects */
       if ((jmed==1) & (i==1) & (nms==0) & (modcok==1))
       {
        contvec2=matrix(1,2,1)
        contvec2<-cbind(contvec2,wcontval,zcontval)
        if (wzcmat[(i+1),jmed]==1)
        {
         for (conti in (1:ncol(wcontval)))
         {
          for (contj in (1:ncol(zcontval)))
          {contvec2<-cbind(contvec2,wcontval[,conti]*zcontval[,contj])}
         }
        }
        conteff<-contvec2%*%probcoef
        contdiff<-matrix(contvec2[1,]-contvec2[2,])
        contse<-sqrt(t(contdiff)%*%probvarb%*%contdiff)
        conteffd<-conteff[1,1]-conteff[2,1]
        contvec=cbind(contvec,conteff)
        contvecm<-contvec
        outformres<-process.outform3(contvecm,maxresm,resultm)
        maxresm<-unlist(outformres[2]);resultm<-matrix(unlist(outformres[1]),ncol=maxresm)
        contvecm<-noquote(matrix(sprintf(decimals,contvecm),nrow=nrow(contvecm)))
        rownames(contvecm)<-c("Effect1:","Effect2:")
        colnames(contvecm)<-problabs[1:3]
        if (outscreen==1)
        {cat("\nContrast between conditional effects of X:\n")
        print(contvecm,right=T)}
        if (ydich==0)
        {
         p<-2*pt(-abs(conteffd/contse),df=dfres)
         contvec<-cbind(conteffd,contse,conteffd/contse, p)
         contvec<-cbind(contvec,(conteffd-(tval*contse)))
         contvec<-cbind(contvec,(conteffd+(tval*contse)))
         contlabs<-c("Contrast", hclab, "t", "p", "LLCI", "ULCI")
        }
        if (ydich==1)
        { 
         p<-2*(1-pnorm(abs(conteffd/contse)))
         contvec<-cbind(conteffd,contse,conteffd/contse, p)
         contvec<-cbind(contvec,(conteffd-(xp2*contse)))
         contvec<-cbind(contvec,(conteffd+(xp2*contse)))
         contlabs<-c("Contrast", "se", "Z", "p", "LLCI", "ULCI")
        } 
        outformres<-process.outform3(contvec,maxresm,resultm)
        maxresm<-unlist(outformres[2]);resultm<-matrix(unlist(outformres[1]),ncol=maxresm)
        contvec<-noquote(matrix(sprintf(decimals,contvec),nrow=nrow(contvec)))
        colnames(contvec)<-contlabs
        rownames(contvec)<-" "
        if (outscreen==1)
        {cat("\nTest of Effect1 minus Effect2\n")
        print(contvec,right=T)}
       }
      }
      # end O

	if ((plot==1) | (plot==2))
      { 
       datalabs<-cbind(t(focpred[,2]),outnames[i,1])
       if (plot==2)
       {datalabs<-c(datalabs,"se", "LLCI", "ULCI")}
       if ((i==(nms+nys)) & (ydich==1))
       {datalabs<-c(datalabs,"prob")}
       outformres<-process.outform3(probeplt,maxresm,resultm)
       maxresm<-unlist(outformres[2]);resultm<-matrix(unlist(outformres[1]),ncol=maxresm)
       probepnt<-noquote(matrix(sprintf(decimals,probeplt),nrow=nrow(probeplt)))
       colnames(probepnt)<-datalabs
       rownames(probepnt)<-t(matrix(replicate(nrow(probepnt)," ")))
       if (outscreen==1)
       {cat("\nData for visualizing the conditional effect of the focal predictor:\n")
       print(probepnt,right=T)}
      }    
     }      
     #End E
    }
    #End R
   }
   #End PROBEandPLOT

    if ((model==74) & (i <= nms))
    {
      onetemp<-matrix(1,nrow(xprobval),1)
      mestmt74<-cbind(onetemp,xprobval)
      if (ncs > 0)
       {
        ncovmdl<-sum(ccmat[i,])
        if (ncovmdl > 0)
        {
          cvmnc<-matrix(1,nrow(mestmt74),ncovmdl)
          cvmnctmp<-t(matrix(apply(matrix(x[,(ncol(x)-ncovmdl+1):ncol(x)],nrow=nrow(x)),2,mean)))
          if (cuscoval > 0){cvmnctmp<-coval}            
          for (mestlp in c(1:ncovmdl))
          {cvmnc[,mestlp]<-cvmnc[,mestlp]*cvmnctmp[,mestlp]}
          mestmt74<-cbind(mestmt74,cvmnc)
        }
       }
      mest74t<-mestmt74%*%b
      if (i==1){mest74<-mest74t}
      if (i > 1){mest74<-cbind(mest74,mest74t)}      
    }

    #linear sum
    if (ydich==0)
    {
     if ((i==(nms+nys)) & (model >= 0) & (model < 4) & (linsum[1,1] != -999))
     {
      lhyprob<-1;meansub<-0
      if (((nlinsum==nrow(b)) | (nlinsum==(nrow(b)-ncs))))
      {
       if ((nlinsum ==(nrow(b)-ncs)) & (ncs > 0))
       {
        linsum<-cbind(linsum,covmeans)
        meansub<-1
       }
       lhyprob<-0;hypest<-linsum%*%b
       sehypest<-sqrt(linsum%*%varb%*%t(linsum))
       phypest<-2*(pt((-abs(hypest/sehypest)),dfres))
       hypest<-matrix(c(hypest,sehypest,(hypest/sehypest),phypest,(hypest-tval*sehypest),(hypest+tval*sehypest)),ncol=6)
       outformres<-process.outform3(t(linsum),maxresm,resultm,1)
       maxresm<-unlist(outformres[2]);resultm<-matrix(unlist(outformres[1]),ncol=maxresm)
       outformres<-process.outform3(hypest,maxresm,resultm)
       maxresm<-unlist(outformres[2]);resultm<-matrix(unlist(outformres[1]),ncol=maxresm)
       if (outscreen==1)
       {
        hyplabs<-c("Estimate",hclab,"t","p","LLCI","ULCI")
        cat("\n----------\n")
        cat("Linear Combination Estimate and Hypothesis Test\n")
        linsumpt<-noquote(matrix(sprintf(decimals,linsum),ncol=1))
        colnames(linsumpt)<-"weight"
        rownames(linsumpt)<-vlabsm
        cat("\nWeight vector:\n")
        print(linsumpt,right=TRUE)
        hypestpt<-noquote(matrix(sprintf(decimals,hypest),ncol=6))
        colnames(hypestpt)<-hyplabs
        rownames(hypestpt)<-" "
        cat("\n")
        print(hypestpt,right=TRUE)
        if (meansub==1)
        {cat("\nCovariate weight(s) set to the sample mean.\n")}
       }
      }
      if (lhyprob==1){notecode[notes,1]<-30;notes<-notes + 1}
     }
    }
  }
  #END G LOOP
  lastb<-b
  lastcov<-varb
  if ((criterr==0) & (dototal==1))
  {
   x<-xtmp
   vlabsm<-matrix(c("constant",xcatlab[1:nxvls,1]))
   if (ncs > 0)
   {
    x<-cbind(as.matrix(x),as.matrix(ctmp))
    vlabsm<-matrix(c(vlabsm,t(covnames)))
   }
   x<-cbind(ones,x)
   modoutz<-process.modelest(y,x,1,1,xp2,hc)
   modsum<-matrix(unlist(modoutz[3]))
   modres<-matrix(unlist(modoutz[1]),nrow=ncol(x))
   toteff<-matrix(modres,nrow=ncol(x))
   toteff<-matrix(toteff[2:(1+nxvls),],nrow=nxvls)
   nodotot<-0
   if ((xdich==1) & (xmint==1) & (model==74))
   {
    toteff[,1]<-toteff[,1]*xscaling
    toteff[,2]<-toteff[,2]*abs(xscaling)
    toteff[,3]<-toteff[,3]*xscaling
    toteff[,5]<-toteff[,5]*xscaling
    toteff[,6]<-toteff[,6]*xscaling
    citmp<-t(matrix(toteff[,5:6]))
    if (xscaling < 0)
    {
     toteff[,5]<-citmp[,2]
     toteff[,6]<-citmp[,1]
    }
    nodotot<-1
   }
   if (outscreen==1)
   {
    if (nodotot==0)
    {cat("\n************************ TOTAL EFFECT MODEL *************************** \n")}
    if (nodotot==1)
    {cat("\n*********************************************************************** \n")}
    cat("Outcome Variable: ")
    write.table(outnames[nrow(outnames),1],quote=FALSE,row.names=FALSE,col.names=FALSE)
    cat("\n")
   }
   outformres<-process.outform3(modsum,maxresm,resultm)
   maxresm<-unlist(outformres[2]);resultm<-matrix(unlist(outformres[1]),ncol=maxresm)
   modsum<-noquote(matrix(sprintf(decimals,modsum),nrow=1))
   outformres<-process.outform3(modres,maxresm,resultm)
   maxresm<-unlist(outformres[2]);resultm<-matrix(unlist(outformres[1]),ncol=maxresm)
   modres<-noquote(matrix(sprintf(decimals,modres),nrow=ncol(x)))
   varb<-matrix(unlist(modoutz[6]),nrow=ncol(x))
   brsq2<-modsum[1,2]
   b<-matrix(unlist(modoutz[5]))
   colnames(modsum)<-modsuml;rownames(modsum)<-" "
   rownames(modres)<-vlabsm;colnames(modres)<-modresl
   if (outscreen==1)
   {
    cat("Model Summary: \n")
    print(modsum,right=T)
    cat("\n")
    cat("Model: \n")
    print(modres,right=T)
   }   
   totefflb<-modresl
   toteffl2<-vlabsm[2:(1+nxvls),]
   lmat<-matrix(0,nrow(b),1)
   lmat2<-matrix(1,nxvls,1)
   lmat[2:(1+nxvls),1]<-lmat2
   if (ydich != 1)
   {totomni<-process.ftest3(lmat,b,varb,1,brsq2,0,y,x)} 
   if (stand==1)
   {
    predsd<-matrix(0,nrow(modres),1)
    stdmod<-as.numeric(modres[,1])/ovsd[nrow(ovsd),1]
    for (jd in c(1:ncol(x)))
    {predsd[jd,1]<-sd(x[,jd])}   
    if ((wherex[1,ncol(wherex)] != -999) & ((nxvls > 1) | (xdich==1)))  
    {  
     sdmsone<-matrix(1,nxvls,1)
     predsd[wherex[1,ncol(wherex)]:wherex[2,ncol(wherex)],1]<-sdmsone    
     pstog<-1    
    }
    predsd[1,1]<-1
    stdmod<-stdmod*predsd
    stdmod<-matrix(stdmod[2:nrow(stdmod),1])
    sdvlabs<-vlabsm[2:nrow(vlabsm),1]
    outformres<-process.outform3(stdmod,maxresm,resultm,1)
    maxresm<-unlist(outformres[2]);resultm<-matrix(unlist(outformres[1]),ncol=maxresm)
    stdmod<-noquote(matrix(sprintf(decimals,stdmod),(nrow(b)-1)))
    colnames(stdmod)<-"coeff"
    rownames(stdmod)<-sdvlabs
    if (outscreen==1)
    {cat("\nStandardized coefficients:\n")
     print(stdmod,right=T)}
   }   

   if (covcoeff==1)
   {
    varbpr<-noquote(matrix(sprintf(decimals,varb),nrow=nrow(b)))
    rownames(varbpr)<-vlabsm;colnames(varbpr)<-vlabsm
    if (outscreen==1)
    {
     cat("\nCovariance matrix of regression parameter estimates:\n")
     print(varbpr,right=T)
     outformres<-process.outform3(varb,maxresm,resultm)
     maxresm<-unlist(outformres[2]);resultm<-matrix(unlist(outformres[1]),ncol=maxresm)     
    }
   } 
  }
 }
 #END cycle through the models


   


 if ((criterr==0) & (nms > 0) & (ydich==0) & (modelres==1))
 {
  modresid<-matrix(modresid[,2:ncol(modresid)],ncol=(ncol(modresid)-1))
  corall<-cor(modresid)
  outformres<-process.outform3(corall,maxresm,resultm)
  maxresm<-unlist(outformres[2]);resultm<-matrix(unlist(outformres[1]),ncol=maxresm)
  corall2<-noquote(matrix(sprintf(decimals,corall),nrow=nrow(corall)))
  colnames(corall2)<-outnames
  rownames(corall2)<-outnames
  if (outscreen==1)
  {cat("\n*************** CORRELATIONS BETWEEN MODEL RESIDUALS **************** \n\n")
  print(corall2,right=T)}
 }

 #DO BOOTSTRAPPING
 if ((criterr==0) & (boot > 0))
 { 
  bootres<-matrix(-999,1,sum(nump))
  bootdir<-obsdirfx
  natdirbt<-matrix(-999,1,nxvls)
  if (effsize==1){bootysd<-matrix(-999,1,1);bootxsd<-matrix(-999,1,1)}
  badboot<-0;goodboot<-0;smallest<-1;booting<-1
  j<-1
  if (outscreen==1)
  {
   cat("\n*********************************************************************** \n")
   if (progress==1)
   {cat("Bootstrapping progress:\n")
   bootprog<-txtProgressBar(min=0,max=boot,char=">",width=62,style=3)}
   if (progress !=1){cat("Bootstrapping in progress. Please wait.\n");flush.console()}
  }
  while ((goodboot < boot) & (j <= maxboot))
  {
   if ((outscreen==1) & (progress==1)){setTxtProgressBar(bootprog,j)}   
   nobootx<-1;modres2<-999;bad<-0;
   v<-as.matrix(trunc(runif(n)*n)+1)
   for (i in (1:(nms+nys)))
   {
    y<-as.matrix(outvars[v,i])
    ynovar<-sum((y-(sum(y)/nrow(y)))*(y-(sum(y)/nrow(y))))
    if (ynovar==0){bad<-1}
    xindx<-datindx[1:(nump[1,i]-1),i]
    hello<-0
    x<-as.matrix(fulldat[v,xindx])
    x<-cbind(ones,x)
    xsq<-t(x)%*%x
    exsq<-eigen(xsq)
    exsq<-matrix(unlist(exsq[1]))
    holymoly<-min(exsq)
    zeroeig<-sum(as.numeric(exsq <= 0.000000000002))
    bad<-bad+as.numeric(zeroeig > 0)
    bad=bad+as.numeric(sd(y)==0)
    if (bad==0)
    {
     if (holymoly < smallest){smallest<-holymoly}
     if ((ydich==0) | (i < (nms+nys)))
     {modrest<-process.modelest(y,x,type=1,full=0,xp2,hc)}
     if ((ydich==1) & (i==(nms+nys)))
     {modrest<-process.modelest(y,x,3,0,xp2,hc,iterate,converge)}
     modres2<-as.matrix(c(modres2,modrest))
     if (i==(nms+nys))
     {
      if (bcmat[(i+1),1]==1)
      {bootdir<-rbind(bootdir,t(modrest[wherex[1,i]:wherex[2,i],1]))}             
      if (bcmat[(i+1),1]==0)
      {bootdir<-rbind(bootdir,dirzes)}         
     }
                   
     if ((model==74) & (i <= nms))
     {
      onetemp<-matrix(1,nrow(xprobval),1)
      mestmtb<-cbind(onetemp,xprobval)
      if (ncs > 0)
      {        
       ncovmdlb<-sum(ccmat[i,])
       if (ncovmdl > 0)
       {
        cvmncb<-matrix(1,nrow(mestmtb),ncovmdlb)
        cvmnctmp<-t(matrix(apply(matrix(x[,(ncol(x)-ncovmdl+1):ncol(x)],nrow=nrow(x)),2,mean)))                
        if (cuscoval > 0){cvmnctmp<-coval}
        for (mestlp in c(1:ncovmdlb))
        {cvmncb[,mestlp]<-cvmncb[,mestlp]*cvmnctmp[,mestlp]}
        mestmtb<-cbind(mestmtb,cvmncb)
       }
      }
      mestbt<-mestmtb%*%modrest
      if (i == 1){mestb<-mestbt}
      if (i > 1){mestb<-cbind(mestb,mestbt)}
     }
     if ((model==74) & (i==(nms+nys)))
     {
      xvalptmp<-matrix(0,1,nxvls)
      mest74sp<-matrix(0,1,nms)
      mest74sp<-rbind(mest74sp,mestb)
      xvalptmp<-rbind(xvalptmp,diag(nxvls))
      dirfxcf<-matrix(0,nrow(mestb),1)         
      for (cfloop1 in (1:nrow(mestb)))
      {
       ndirfx<-matrix(0,nrow(modrest),1)
       ndirfx[2:(nxvls+1),1]<-t(xvalptmp[cfloop1,])
       for (cfloop3 in (1:nms))
       {
        if ((mcx==1) | (mcx==0))
        {ndirfx[wheremw[(1+((cfloop3-1)*2)),ncol(wheremw)]:wheremw[(2+((cfloop3-1)*2)),ncol(wheremw)]]<-t(xvalptmp[cfloop1,]*mestb[1,cfloop3])}
        if (mcx==2)
        {ndirfx[wheremw[(1+((cfloop3-1)*2)),ncol(wheremw)]:wheremw[(2+((cfloop3-1)*2)),ncol(wheremw)]]<-t(xvalptmp[cfloop1,]*mest74sp[cfloop1,cfloop3])}
       }
       if (cfloop1 > 1){dirfxcf[cfloop1,1]<-t(ndirfx)%*%modrest*xscaling}
      }
      natdirbt<-rbind(natdirbt,t(dirfxcf[2:nrow(dirfxcf),]))
     }
           
     if ((bcmat[(i+1),1]==1) & (nobootx==1) & (effsize==1))
     {
      nobootx<-0
      xsdtemp<-sum((x[,2]-(sum(x[,2])/nrow(x)))*(x[,2]-(sum(x[,2])/nrow(x))))
      xsdtemp<-sqrt(xsdtemp/(nrow(x)-1))
     }
    }
   }
   if (bad==0)
   {
    modres2<-t(modres2[2:nrow(modres2),])
    #modres2<-matrix(modres2[,2:nrow(modres2),ncol=ncol(modres)-1)
    bootres<-rbind(bootres,modres2)
    if (effsize==1)
    {
     ysdtemp<-sd(y)
     bootysd<-rbind(bootysd,ysdtemp)
     bootxsd<-rbind(bootxsd,xsdtemp)
    }
    goodboot<-goodboot+1
   }
   if (bad != 0){badboot<-badboot+1}
   j<-j+1
  }
  bootres<-as.matrix(bootres[2:nrow(bootres),])
  if (effsize==1)
  {
   bootysd<-matrix(bootysd[2:nrow(bootysd),])
   if (nrow(bootxsd) > 1){bootxsd<-matrix(bootxsd[2:nrow(bootxsd)])}
  }
  if (goodboot < (boot))
  {boot<-0;modelbt<-0;notecode[notes,1]<-7;notes<-notes+1}
  if (boot > 0)
  {
   if (effsize==1){bootysd<-rbind(ysd,bootysd);bootxsd<-rbind(xsd,bootxsd)}
   if (saveboot==1)
   {
    savlabs<-matrix(" ",ncol(bootres))
    for (i in c(1:ncol(bootres))){savlabs[i,1]<-paste("col",i,sep='')}
    boots<-as.data.frame(bootres)
    colnames(boots)<-savlabs
    #assign("process.boots", boots, envir = .GlobalEnv)
   }
   if (modelbt==1)
   {   
    bootcim<-matrix(-99999,ncol(bootres),5)
    bootcim[,2]<-t(colSums(bootres)/nrow(bootres))
    bootcim[,1]<-as.numeric(coeffmat[2:nrow(coeffmat),1])
    for (i in (1:ncol(bootres)))
    { 
     if (bc==0){bootcim[i,3:5]<-t(process.pboot3(bootres[,i],cilow,cihigh))}
     if (bc==1)
     {
      bcbout<-process.bcboot3(bootres[,i],bootcim[i,1],xp2,badend,priorlo,priorhi)
      bootcim[i,3:5]<-t(matrix(unlist(bcbout[1])))
      badend<-unlist(bcbout[2]);priorlo<-unlist(bcbout[3]);priorhi<-unlist(bcbout[4])
     }
    }
   } 
  }

  if (badboot > 0){notecode[notes,1]<-6;notes<-notes+1}
  if ((outscreen==1) & (progress==1)){close(bootprog)}
 }
 #end of bootstrapping
 if ((xmint==1) & (criterr==0))
 {
  xvalptmp<-matrix(0,1,nxvls)
  mest74sp<-matrix(0,1,nms)
  mest74sp<-rbind(mest74sp,mest74)
  xvalptmp<-rbind(xvalptmp,diag(nxvls))
  for (kcfuhd in (1:2))
  {
   dirfxcf<-matrix(0,nrow(mest74),6)
   for (cfloop1 in (1:nrow(mest74)))
   {
    ndirfx<-matrix(0,nrow(lastb),1)
    ndirfx[2:(nxvls+1),1]<-t(xvalptmp[cfloop1,])
    for (cfloop3 in (1:nms))
    {
     if (kcfuhd==1)
     {
      if ((mcx==1) | (mcx==0))
      {ndirfx[wheremw[(1+((cfloop3-1)*2)),ncol(wheremw)]:wheremw[(2+((cfloop3-1)*2)),ncol(wheremw)]]<-t(xvalptmp[cfloop1,]*mest74[1,cfloop3])}
      if (mcx==2)
      {ndirfx[wheremw[(1+((cfloop3-1)*2)),ncol(wheremw)]:wheremw[(2+((cfloop3-1)*2)),ncol(wheremw)]]<-t(xvalptmp[cfloop1,]*mest74sp[cfloop1,cfloop3])}
     }
     if (kcfuhd==2)
      {ndirfx[wheremw[(1+((cfloop3-1)*2)),ncol(wheremw)]:wheremw[(2+((cfloop3-1)*2)),ncol(wheremw)]]<-t(xvalptmp[cfloop1,]*medmeans[1,cfloop3])}
    }
    if (cfloop1 > 1)
    {
     dirfxcf[cfloop1,1]<-t(ndirfx)%*%lastb*xscaling
     dirfxcf[cfloop1,2]<-sqrt(diag(t(ndirfx)%*%lastcov%*%ndirfx))*abs(xscaling)
     dirfxcf[cfloop1,3]<-dirfxcf[cfloop1,1]/dirfxcf[cfloop1,2]
     dirfxcf[cfloop1,4]<-2*pt(-abs(dirfxcf[cfloop1,3]),df=dfres)
     dirfxcf[cfloop1,5]<-dirfxcf[cfloop1,1]-tval*dirfxcf[cfloop1,2]  
     dirfxcf[cfloop1,6]<-dirfxcf[cfloop1,1]+tval*dirfxcf[cfloop1,2]
    }
   }
   codireff<-matrix(dirfxcf[2:nrow(dirfxcf),],nrow=(nrow(dirfxcf)-1))
   if (kcfuhd==1){direff<-codireff}
  }
 }

 #Here is for the indirect effects
 if ((criterr==0) & (nms > 0))
 {
  paths<-matrix(paths[,2:ncol(paths)],nrow=nrow(paths))
  pathsw<-matrix(pathsw[,2:ncol(pathsw)],nrow=nrow(pathsw))
  pathsz<-matrix(pathsz[,2:ncol(pathsz)],nrow=nrow(pathsz))
  pathswz<-matrix(pathswz[,2:ncol(pathswz)],nrow=nrow(pathswz))
  pathsmod<-pathsw+pathsz+pathswz
  pathsdv<-t(pathsdv[2:nrow(pathsdv),])
  pathsfoc<-matrix(pathsfoc[,2:ncol(pathsfoc)],nrow=nrow(pathsfoc))
  pathtype<-matrix(pathtype[,2:ncol(pathtype)],nrow=nrow(pathtype))
  anymod<-as.numeric(sum(pathsmod) > 0)
  obscoeff<-t(as.matrix(obscoeff[1,2:ncol(obscoeff)]))
  if (outscreen==1)
  {
   if (xmint==1)
   {cat("\n********************** COUNTERFACTUALLY DEFINED ***********************")}
   if ((dototal==0) & (alttotal==0))
   {cat("\n**************** DIRECT AND INDIRECT EFFECTS OF X ON Y ****************\n")}
   if (alttotal==1)
   {cat("\n************ TOTAL, DIRECT AND INDIRECT EFFECTS OF X ON Y *************\n")}
  }
  if (dototal==1)
  {
   if (outscreen==1)
   {cat("\n************ TOTAL, DIRECT, AND INDIRECT EFFECTS OF X ON Y ************\n")}   
   totefflb[1,1]<-"effect"
    if (effsize==1)
    {
     toteffsz<-toteff[,1]/ysd 
     if ((xdich==1) | (mcx > 0)){totefflb<-cbind(totefflb,"c_ps")}
     if ((xdich==0) & (mcx==0))
     {toteffsz<-(toteffsz*xsd);totefflb<-cbind(totefflb,"c_cs")}     
     toteff<-cbind(toteff,toteffsz)
    }
    outformres<-process.outform3(toteff,maxresm,resultm)
    maxresm<-unlist(outformres[2]);resultm<-matrix(unlist(outformres[1]),ncol=maxresm)
    toteff2<-noquote(matrix(sprintf(decimals,toteff),nrow=nrow(toteff)))
    colnames(toteff2)<-totefflb
    if (nxvls > 1)
    {
     rownames(toteff2)<-toteffl2
     if (outscreen==1)
     {cat("\nRelative total effects of X on Y:\n")
     print(toteff2,right=T)}
     clabtmp<-c("R2-chng", hcflab, "df1","df2","p")
     outformres<-process.outform3(totomni,maxresm,resultm)
     maxresm<-unlist(outformres[2]);resultm<-matrix(unlist(outformres[1]),ncol=maxresm)
     totomni2<-noquote(matrix(sprintf(decimals,totomni),nrow=nrow(totomni)))
     colnames(totomni2)<-clabtmp
     rownames(totomni2)<-" "
     if (outscreen==1)
     {
      cat("\nOmnibus test of total effect of X on Y:\n")
      print(totomni2,right=T)
      cat("----------\n")
     }
    }
    if (nxvls < 2)
    {
     rownames(toteff2)<-" "
     if (outscreen==1)
     {cat("\nTotal effect of X on Y:\n")
     print(toteff2,right=T)}
    }
   
  }
  moddir<-wcmat[nrow(bcmat),1]+zcmat[nrow(bcmat),1]
  if (xmint==1){moddir<-1}
  if (bcmat[nrow(bcmat),1]==1)
  {
   if (ydich==1)
   {
    direfflb[,(ncol(direfflb)-5):ncol(direfflb)]<-t(matrix(c("Effect","se","Z","p","LLCI","ULCI")))
   } 
   if ((moddir==0) | (xmint==1)){direfflb[1,1]="effect"}
   if ((effsize==1) & (moddir==0) & (anymod == 0))
   {
    direffsz<-direff[,1]/ysd
    if ((xdich==1) | (mcx > 0)){direfflb<-cbind(direfflb,"c'_ps")}
    if ((xdich==0) & (mcx==0))
    {direffsz<-(direffsz*xsd);direfflb<-cbind(direfflb,"c'_cs")}    
    direff<-cbind(direff,direffsz)
   }
   outformres<-process.outform3(direff,maxresm,resultm)
   maxresm<-unlist(outformres[2]);resultm<-matrix(unlist(outformres[1]),ncol=maxresm)
   if (xmint==1)
   {
    outformres<-process.outform3(codireff,maxresm,resultm)
    maxresm<-unlist(outformres[2]);resultm<-matrix(unlist(outformres[1]),ncol=maxresm)
   }
   direff2<-noquote(matrix(sprintf(decimals,direff),nrow=nrow(direff)))
   colnames(direff2)<-direfflb
   if ((moddir==0) & (nxvls==1) & (outscreen==1))
   {
    rownames(direff2)<-" "
    cat("\nDirect effect of X on Y:\n")
    print(direff2,right=T)
   }
   if ((moddir==0) & (nxvls>1))  
   {
    rownames(direff2)<-direffl2
    if (outscreen==1)
    {cat("\nRelative direct effects of X on Y:\n")
    print(direff2,right=T)} 
    outformres<-process.outform3(diromni,maxresm,resultm)
    maxresm<-unlist(outformres[2]);resultm<-matrix(unlist(outformres[1]),ncol=maxresm)
    diromni2<-noquote(matrix(sprintf(decimals,diromni),nrow=nrow(diromni)))
    rownames(diromni2)<-" "
    if (ydich==0)
    {  
     colnames(diromni2)<-c("R2-chng", hcflab, "df1","df2","p")
     if (outscreen==1)
     {cat("\nOmnibus test of direct effect of X on Y:\n")
     print(diromni2,right=T)}
    }
    if (ydich==1)
    {  
     colnames(diromni2)<-c("Chi-sq", "df", "p")
     if (outscreen==1)
     {cat("\nOmnibus likelihood ratio test of direct effect of X on Y:\n")
     print(diromni2,right=T)}
    }
    if (outscreen==1)
    {cat("\n----------\n")}
   }
   if ((moddir > 0) & (nxvls==1))
   {
    if ((xmint==0) & (outscreen==1))
    {cat("\nConditional direct effect(s) of X on Y:\n")}
    if ((xmint==1) & (outscreen==1))
    {
     cat("\n(Pure) Natural direct effect of X on Y:\n") 
    }
    if (xmint==1){obnatdfx<-matrix(direff[,1],nrow=nrow(direff))}
    rownames(direff2)<-t(matrix(replicate(nrow(direff2)," ")))
    if (outscreen==1){print(direff2,right=T)}
    if (xmint==1)
    {
     if ((outscreen==1) & (xmint==1))
     {
     cat("\nControlled direct effect of X on Y:\n")
     codiref2<-noquote(matrix(sprintf(decimals,codireff),nrow=nrow(codireff)))
     rownames(codiref2)<-t(matrix(replicate(nrow(codireff)," ")))
     colnames(codiref2)<-direfflb
     print(codiref2,right=T)
     cat("\n----------\n")
     }
    }
   }
   direffl4<-direffl2
   if ((moddir > 0) & (nxvls>1))
   {
    direffl2=" "
    for (i in (1:nxvls))
    {
     for (j in (1:(nrow(direff)/nxvls)))
     {direffl2<-rbind(direffl2,xcatlab[i,1])}     
    }
    direffl2<-direffl2[2:nrow(direffl2),1]
    rownames(direff2)<-direffl2
    if ((xmint==0) & (outscreen==1))
    {cat("\nRelative conditional direct effects of X on Y:\n")}
    if ((xmint==1) & (outscreen==1))
    {
     cat("\nRelative (pure) natural direct effects of X on Y:\n")      
    }
    if (outscreen==1){print(direff2,right=T)}
    if (xmint==1){obnatdfx<-matrix(direff[,1],nrow=nrow(direff))}
    if ((xmint==1) & (outscreen==1))
    {
     cat("\nRelative controlled direct effects of X on Y:\n")
     codiref2<-noquote(matrix(sprintf(decimals,codireff),nrow=nrow(codireff)))
     rownames(codiref2)<-direffl2
     colnames(codiref2)<-direfflb
     print(codiref2,right=T)
     cat("\n----------\n")
    }    
   }
   direffl2<-direffl4    
  }

   if ((bcmat[nrow(bcmat),1]==0) & (xmint != 1) & (outscreen==1))
   {cat("\nThe direct effect of X on Y is fixed to zero.\n")}
 
  #Here is the start of the indirect effects
  if (nms==1){indmark<-matrix(2);indsets<-t(matrix(c(1,2)))}
  if (nms==2){indmark<-t(matrix(c(2,2,3)));indsets<-t(matrix(c(1,4,2,5,1,3,5)));thetam<-matrix(1)}
  if (nms==3) 
  {indmark<-t(matrix(c(2,2,2,3,3,3,4)))
   indsets<-t(matrix(c(1,7,2,8,4,9,1,3,8,1,5,9,2,6,9,1,3,6,9)))
   thetam<-t(matrix(c(1,2,3)))}
   if (nms==4)
  {indmark<-t(matrix(c(2,2,2,2,3,3,3,3,3,3,4,4,4,4,5)))
   indsets<-t(matrix(c(1,11,2,12,4,13,7,14,1,3,12,1,5,13,1,8,14,2,6)))
   indsets<-cbind(indsets,t(matrix(c(13,2,9,14,4,10,14,1,3,6,13,1,3,9,14,1,5,10,14,2,6,10,14,1,3,6,10,14))))
   thetam<-t(matrix(c(1,2,4,3,5,6)))}
  if (nms==5)
  {indmark<-t(matrix(c(2,2,2,2,2,3,3,3,3,3,3,3,3,3,3,4,4,4,4,4,4,4,4,4,4,5,5,5,5,5,6)))
   indsets<-t(matrix(c(1,16,2,17,4,18,7,19,11,20,1,3,17,1,5,18,1,8,19,1,12,20,2,6,18,2,9,19,2)))
   indsets<-cbind(indsets,t(matrix(c(13,20,4,10,19,4,14,20,7,15,20,1,3,6,18,1,3,9,19,1,3,13,20,1,5,10))))
   indsets<-cbind(indsets,t(matrix(c(19,1,5,14,20,1,8,15,20,2,6,10,19,2,6,14,20,2,9,15,20,4,10,15,20,1))))
   indsets<-cbind(indsets,t(matrix(c(3,6,10,19,1,3,6,14,20,1,3,9,15,20,1,5,10,15,20,2,6,10,15,20,1,3))))
   indsets<-cbind(indsets,t(matrix(c(6,10,15,20))))
   thetam<-t(matrix(c(1,2,5,3,6,8,4,7,9,10)))}
  if (nms==6)
  {indmark<-t(matrix(c(2,2,2,2,2,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,5,5,5,5,5)))
   indmark<-cbind(indmark,t(matrix(c(5,5,5,5,5,5,5,5,5,5,6,6,6,6,6,6,7))))
   indsets<-t(matrix(c(1,22,2,23,4,24,7,25,11,26,16,27,1,3,23,1,5,24,1,8,25,1,12,26,1,17,27,2,6,24,2,9,25,2,13,26,2,18)))
   indsets<-cbind(indsets,t(matrix(c(27,4,10,25,4,14,26,4,19,27,7,15,26,7,20,27,11,21,27,1,3,6,24,1,3,9,25,1,3,13,26,1,3))))
   indsets<-cbind(indsets,t(matrix(c(18,27,1,5,10,25,1,5,14,26,1,5,19,27,1,8,15,26,1,8,20,27,1,12,21,27,2,6,10,25,2,6,14,26))))
   indsets<-cbind(indsets,t(matrix(c(2,6,19,27,2,9,15,26,2,9,20,27,2,13,21,27,4,10,15,26,4,10,20,27,4,14,21,27,7,15,21,27))))
   indsets<-cbind(indsets,t(matrix(c(1,3,6,10,25,1,3,6,14,26,1,3,6,19,27,1,3,9,15,26,1,3,9,20,27,1,3,13,21,27,1,5,10,15,26))))
   indsets<-cbind(indsets,t(matrix(c(1,5,10,20,27,1,5,14,21,27,1,8,15,21,27,2,6,10,15,26,2,6,10,20,27,2,6,14,21,27,2,9,15))))
   indsets<-cbind(indsets,t(matrix(c(21,27,4,10,15,21,27,1,3,6,10,15,26,1,3,6,10,20,27,1,3,6,14,21,27,1,3,9,15,21,27,1,5,10))))
   indsets<-cbind(indsets,t(matrix(c(15,21,27,2,6,10,15,21,27,1,3,6,10,15,21,27))))
   thetam<-t(matrix(c(1,2,6,3,7,10,4,8,11,13,5,9,12,14,15)))}
  if (nms==7)
  {indmark<-t(matrix(c(2,2,2,2,2,2,2)));indsets<-t(matrix(c(1,29,2,30,4,31,7,32,11,33,16,34,22,35)))}
  if (nms==8)
  {indmark<-t(matrix(c(2,2,2,2,2,2,2,2)));indsets<-t(matrix(c(1,37,2,38,4,39,7,40,11,41,16,42,22,43,29,44)))}
  if (nms==9)
  {indmark<-t(matrix(c(2,2,2,2,2,2,2,2,2)));indsets<-t(matrix(c(1,46,2,47,4,48,7,49,11,50,16,51,22,52,29,53,37,54)))}
  if (nms==10)
  {indmark<-t(matrix(c(2,2,2,2,2,2,2,2,2,2))) 
  indsets<-t(matrix(c(1,56,2,57,4,58,7,59,11,60,16,61,22,62,29,63,37,64,46,65)))}
  indlbl<-"Ind1"
  for (indb in (2:90))
  {indlbl<-cbind(indlbl,paste("Ind",indb,sep=''))}
  indlbl<-matrix(indlbl)
  cntname<-"(C1)"
  for (indb in (2:105))
  {cntname<-cbind(cntname,paste("(C",indb,")",sep=''))}
  cntname<-matrix(cntname)
  indmake<-matrix(0,ncol(indmark),(nms+2))
  indmod<-matrix(999,ncol(indmark),1)
  indmmm<-matrix(0,ncol(indmark),1)
  indmmmt<-matrix(0,ncol(indmark),1)
  start<-1;end<-0;nindfx<-0
  indlocs<-matrix(999,nrow(thetaxmb),ncol(paths))
  indkey<-matrix("      ",ncol(indmark),1+((max(indmark)*2)+1))
  c1<-1;c2<-1;c3<-1
  for (i in (1:ncol(paths)))
  {
   if (pathtype[1,i]==1){indlocs[,i]<-thetaxmb[,c1];c1<-(c1+1)}
   if (pathtype[1,i]==3){indlocs[,i]<-thetamyb[,c2];c2<-(c2+1)}
   if ((pathtype[1,i]==2) & (nms < 7) & (serial==1)){indlocs[,i]<-thetammb[,thetam[1,c3]];c3<-(c3+1)}
  }
  for (i in (1:ncol(indlocs)))
  {
   c1<-2
   for (j in (2:nrow(indlocs)))
   {
    if (indlocs[j,i] != 0){indlocs[c1,i]<-indlocs[j,i];c1<-(c1+1)}
   }
   indlocs[1,i]<-(c1-2)
  }
  indlocs<-indlocs[1:max((indlocs[1,])+1),]
  for (i in (1:ncol(indmark)))
  { 
   numget<-indmark[1,i];end<-end+numget;gotcha<-t(matrix(indsets[1,start:end]))
   start<-end+1;ok<-1;temp<-0;repoman<-matrix(0,4,1)
   for (j in (1:ncol(gotcha)))
   {
    if (paths[1,gotcha[1,j]]==0){ok<-0}
    if (pathsmod[1,gotcha[1,j]] > 0)
    {
     temp<-1
     temp2<-rbind(pathsw[1,gotcha[1,j]],pathsz[1,gotcha[1,j]],pathswz[1,gotcha[1,j]],0)
     temp2<-matrix(temp2)
     if ((temp2[1,1]==1) & (temp2[2,1]==1) & (temp2[3,1]==0)){temp2[4,1]<-1}
     repoman<-repoman+temp2
    }
   }
   temp<-0;tempmmm<-0;typemmm<-0
   if ((repoman[1,1] > 0) & (repoman[2,1]==0))
   {
    temp<-1
    if (repoman[1,1]==1){tempmmm<-1} 
    if ((repoman[1,1] > 1) & ((wdich==1) | (mcw > 0)))
    {
     tempmmm<-12;typemmm<-mcw
     if (wdich==1){typemmm<-1}
    }
    if ((repoman[1,1] > 1) & ((wdich==0) & (mcw==0))){tempmmm<-101} 
   }
   if ((repoman[1,1]==0) & (repoman[2,1] > 0))
   {
    temp<-2
    if (repoman[2,1]==1){tempmmm<-2}
    if ((repoman[2,1] > 1) & ((zdich==1) | (mcz > 0)))
    {
     tempmmm<-22;typemmm<-mcz
     if (zdich==1){typemmm<-1}
    }
    if ((repoman[2,1] > 1) & ((zdich==0) & (mcw==0))){tempmmm<-102}
   }
   if ((repoman[1,1] > 0) & (repoman[2,1] > 0))
   {
    temp<-3
    if ((repoman[1,1]==1) & (repoman[2,1]==1))
    {
     if (repoman[4,1]==1){tempmmm<-31}
     if (repoman[3,1]==1){tempmmm<-41}
    }
   }
   if ((repoman[1,1]==1) & (repoman[2,1]==1) & (repoman[3,1]==0) & (repoman[4,1]==0)){tempmmm<-51}
   if (ok==1)
   {
    nindfx<-nindfx+1
    indmake[nindfx,1]<-numget;indmod[nindfx,1]<-temp;indmmm[nindfx,1]<-tempmmm
    indmmmt[nindfx,1]<-typemmm;indmake[nindfx,2:(1+numget)]<-gotcha;indkey[nindfx,1]<-xnames
    for (j in (1:numget))
    {
     indkey[nindfx,(j*2+1)]<-pathsdv[1,gotcha[1,j]]
     indkey[nindfx,(j*2)]<-"   ->   "
    }
   }
  }
  indkey<-matrix(indkey[1:nindfx,1:((max(indmake[,1])*2)+1)],nrow=nindfx)
  indmake<-matrix(indmake[1:nindfx,1:(max(indmake[,1])+1)],ncol=(max(indmake[,1])+1))
  indmod<-as.matrix(indmod[1:nrow(indmake),1])
  indmmm<-as.matrix(indmmm[1:nrow(indmake),1])
  indmmmt<-as.matrix(indmmmt[1:nrow(indmake),1])
  ncpairs<-(((nindfx)*(nindfx-1))/2)
  if (((contrast==1) | (contrast==2)) & (ncpairs > 105))
  {contrast<-0;notecode[notes,1]<-13;notes<-notes+1}
  if (contrast==3)
  {
   if (ncol(contvec) != nindfx)
   {contrast<-0;notecode[notes,1]<-14;notes<-notes+1}
  }

  #This is for models with no moderator */
  if (anymod==0)
  {
   if ((nms==1) & (contrast > 0)){contrast<-0}
   efloop<-(((1-as.numeric(effsize==0))*2)+1)-((((mcx>0) | (xdich==1)))*(1-as.numeric(effsize==0)))
   for (kk in (1:efloop))
   {
    if (boot==0)
    {
     bootres<-obscoeff
     if (kk==1){totbtvec<-matrix(0,1,nxvls)}
     indtab<-matrix(999);inddiff<-matrix(999);bootysd<-matrix(ysd);bootxsd<-matrix(xsd)
    }    
    if (boot > 0)
    {
     bootres<-rbind(obscoeff,bootres)
     if (kk==1){totbtvec<-matrix(0,nrow(bootres),nxvls)}
     indtab<-matrix(999,1,4);inddiff<-matrix(999,nrow(bootres))
    }
    indtotal<-matrix(0,nrow(bootres),1)
    for (i in (1:nrow(indmake)))
    {
     for (j in (1:nxvls))
     {
      indtemp<-matrix(1,nrow(bootres),1)
      for (k in (1:indmake[i,1]))
      {
       jtemp<-1
       if ((j > 1) & (k==1)){jtemp<-j}
       indtemp<-indtemp*bootres[,pathsfoc[jtemp,indmake[i,(k+1)]]]
      }
      if (kk==2){indtemp<-indtemp/bootysd}
      if (kk==3){indtemp<-((bootxsd*indtemp)/bootysd)}
      if (contrast != 0){inddiff<-cbind(inddiff,indtemp)}
      if (nxvls==1){indtotal<-(indtotal+indtemp)}
      indeff<-indtemp[1,1]
      if (kk==1){totbtvec[,j]<-totbtvec[,j]+indtemp}
      if (boot > 0)
      {
       if (bc==0){bcitmp<-process.pboot3(indtemp[2:nrow(indtemp),1],cilow,cihigh)}
       if (bc==1)
       {
        bcbout<-process.bcboot3(indtemp[2:nrow(indtemp),1],indtemp[1,1],xp2,badend,priorlo,priorhi)
        bcitmp<-matrix(unlist(bcbout[1]))
        badend<-unlist(bcbout[2]);priorlo<-unlist(bcbout[3]);priorhi<-unlist(bcbout[4])
       }
       indeff<-cbind(indeff,t(bcitmp))
      }
      if (kk==1){indtabn<-rbind(indtab,indeff)}
      indtab<-rbind(indtab,indeff)
     }
    }
    indtab<-matrix(indtab[2:nrow(indtab),],nrow=(nrow(indtab)-1))
    if (kk==1){indtabn<-matrix(indtabn[2:nrow(indtabn),],nrow=(nrow(indtabn)-1))}
    rowlbs<-matrix(indlbl[1:nrow(indtab),1])
    rowbls3<-rowlbs
    if (mc > 0)
    {
     inddiff<-matrix(-999,mc,1)
     indtab2<-matrix(-999,nrow(indtab),4)
     indtab2[,1]<-indtab;indtab<-indtab2;mcct<-0
     indtotal<-matrix(0,mc,1)
     if (kk==1)
     {
      x1<-sqrt(-2*log(matrix(runif(mc*nrow(mcsopath)),mc,nrow(mcsopath))))*cos((2*3.14159265358979)*matrix(runif(mc*nrow(mcsopath)),mc,nrow(mcsopath)))
      x1<-x1%*%chol(indcov)
      for (ii in (1:nrow(x1))){x1[ii,]<-(x1[ii,]+t(mcsopath))}
     }
     for (ii in (1:nms))
     {
      tmpb<-x1[,((nms*nxvls)+ii)];tmpb2<-tmpb
      if (nxvls > 1)
      {
       for (jj in (1:(nxvls-1)))
       {tmpb2<-cbind(tmpb2,tmpb)}        
      }
      indtemp<-as.matrix(x1[,(((ii-1)*nxvls)+1):(ii*nxvls)]*tmpb2)
      for (jj in (1:ncol(indtemp)))
      {
       if (kk==2){indtemp[,jj]<-indtemp[,jj]/ysd}
       if (kk==3){indtemp[,jj]<-(xsd*indtemp[,jj])/ysd}
       mcicon<-process.pboot3(indtemp[,jj],cilow,cihigh)
       mcct<-mcct+1
       indtab[mcct,2:4]<-t(mcicon)
      }
      if (nxvls==1)
      {
       indtotal<-indtotal+indtemp
       if (contrast != 0){inddiff=cbind(inddiff,indtemp)}
      }
     }
    }
    if ((normal==1) & (sobelok==1))
    {
     sobelmat<-matrix(indtab[,1])
     sobelmat<-cbind(sobelmat,(sobelmat/2),sobelmat,sobelmat)
     for (ii in (1:nms))
     {
      se2b<-(indcov[((nms*nxvls)+ii),((nms*nxvls)+ii)])
      bpath2<-(mcsopath[((nms*nxvls)+ii),1])^2
      se2a<-matrix(diag(matrix(indcov[(((ii-1)*nxvls)+1):(ii*nxvls),(((ii-1)*nxvls)+1):(ii*nxvls)],nrow=nxvls)))
      apath2<-matrix(mcsopath[(((ii-1)*nxvls)+1):(ii*nxvls),1])^2
      sesobel<-sqrt(apath2*se2b+bpath2*se2a+se2a*se2b)
      sobelmat[(((ii-1)*nxvls)+1):(ii*nxvls),2]<-sesobel
     }
     sobelmat[,3]<-sobelmat[,1]/sobelmat[,2]
     sobelmat[,4]<-2*(1-pnorm(abs(sobelmat[,3])))
    }
    if (serial==0){rowlbs<-t(mnames)}
    if ((nxvls==1) & (nms > 1))
    {
     rowlbs<-rbind("TOTAL",rowlbs)
     indtemp<-indtotal[1,1]
     if ((boot > 0) & (nxvls==1))
     {
      if (bc==0){bcitmp<-process.pboot3(indtotal[2:nrow(indtotal),1],cilow,cihigh)}
      if (bc==1)
      {
       bcbout<-process.bcboot3(indtotal[2:nrow(indtotal),1],indtotal[1,1],xp2,badend,priorlo,priorhi)
       bcitmp<-matrix(unlist(bcbout[1]))
       badend<-unlist(bcbout[2]);priorlo<-unlist(bcbout[3]);priorhi<-unlist(bcbout[4])
      }
      indtemp<-cbind(indtemp,t(bcitmp))
     }
     if (mc > 0)
     {
      obtmc<-indtab[,1];indtemp<-sum(obtmc)
      mcicon2<-process.pboot3(indtotal[,1],cilow,cihigh)
      indtemp<-cbind(indtemp,t(mcicon2))
     }
     indtab<-rbind(indtemp,indtab)
    }
    bootlbs<-"Effect"
    if (boot > 0){bootlbs<-c("Effect","BootSE","BootLLCI","BootULCI")}
    if (mc > 0){bootlbs<-c("Effect","MC SE","MC LLCI","MC ULCI")}
    if (nxvls==1)
    {
     if (contrast != 0)
     {
      inddiff<-matrix(inddiff[,2:ncol(inddiff)],nrow=nrow(inddiff))
      if (mc > 0){inddiff=rbind(t(obtmc),inddiff)}
      if (contrast==3)
      {
       inddifft<-matrix(inddiff%*%t(contvec))
       indtemp<-inddifft[1,1]
       if ((boot > 0) | (mc > 0))
       {
        if ((mc > 0) | ((boot > 0) & (bc==0)))
        {bcicon<-process.pboot3(inddifft[2:nrow(inddifft),1],cilow,cihigh)}
        if ((boot > 0) & (bc==1))
        {
         bcbout<-process.bcboot3(inddifft[2:nrow(inddifft),1],inddifft[1,1],xp2,badend,priorlo,priorhi)
         bcicon<-matrix(unlist(bcbout[1]))
         badend<-unlist(bcbout[2]);priorlo<-unlist(bcbout[3]);priorhi<-unlist(bcbout[4])
        }
        indtemp<-cbind(indtemp,t(bcicon))
       }
       indtab<-rbind(indtab,indtemp)
      }
      if ((contrast==1) | (contrast==2))
      {
       conkey<-matrix(" ",1,4)
       for (i in (1:(ncol(inddiff)-1)))
       {     
        for (j in ((i+1):ncol(inddiff)))
        {           
         inddifft<-matrix(inddiff[,i]-inddiff[,j])
         if (contrast==2){inddifft<-matrix(abs(inddiff[,i])-abs(inddiff[,j]))}
         indtemp<-inddifft[1,1]
         conkeyt<-cbind(" ", rowlbs[(i+1),1]," minus  ",rowlbs[(j+1),1])
         conkey<-rbind(conkey,conkeyt)
         if ((boot > 0) | (mc > 0))
         {
          if ((mc > 0) | ((boot > 0) & (bc==0)))
          {bcitmp2<-process.pboot3(inddifft[2:nrow(inddifft),1],cilow,cihigh)}
          if ((boot > 0) & (bc==1))
          {
           bcbout<-process.bcboot3(inddifft[2:nrow(inddifft),1],inddifft[1,1],xp2,badend,priorlo,priorhi)
           bcitmp2<-matrix(unlist(bcbout[1]))
           badend<-unlist(bcbout[2]);priorlo<-unlist(bcbout[3]);priorhi<-unlist(bcbout[4])
          }
          indtemp<-cbind(indtemp,t(bcitmp2)) 
         }
         indtab<-rbind(indtab,indtemp)
        }
       }
      }
      if (contrast != 3)
      {contlbs<-matrix(cntname[1:(((nindfx)*(nindfx-1))/2),1])}
      if (contrast==3)
      {contlbs<-matrix("(C1)")}    
      rowlbs<-rbind(rowlbs,contlbs)
     }
     outformres<-process.outform3(indtab,maxresm,resultm,1)
     maxresm<-unlist(outformres[2]);resultm<-matrix(unlist(outformres[1]),ncol=maxresm)
     indtab5<-noquote(matrix(sprintf(decimals,indtab),nrow=nrow(indtab)))
     colnames(indtab5)<-bootlbs
     rownames(indtab5)<-rowlbs
     if (outscreen==1)
     {
      if (kk==1)
      {
       cat("\nIndirect effect(s) of X on Y:\n")
       print(indtab5,right=T)
      }

      if ((kk==2) & ((xdich==1) | (mcx > 0)))
      {
       cat("\nPartially standardized indirect effect(s) of X on Y:\n")
       print(indtab5,right=T)
      }
      if (kk==3)
      {
       cat("\nCompletely standardized indirect effect(s) of X on Y:\n")
       print(indtab5,right=T)
      }
     }
     if ((normal==1) & (sobelok==1) & (kk==1))
     {
      sobellab<-c("Effect",hclab,"Z","p")
      sobelrlb<-rowlbs
      if (nms > 1){sobelrlb<-matrix(rowlbs[2:(1+nms),1])}
      outformres<-process.outform3(sobelmat,maxresm,resultm)
      maxresm<-unlist(outformres[2]);resultm<-matrix(unlist(outformres[1]),ncol=maxresm)
      sobelmt2<-noquote(matrix(sprintf(decimals,sobelmat),nrow=nrow(sobelmat)))
      rownames(sobelmt2)<-sobelrlb
      colnames(sobelmt2)<-sobellab
      if (outscreen==1)
      {cat("\nNormal theory test for indirect effect(s):\n")
      print(sobelmt2,right=T)}
     }
     if (contrast != 0)
     {
      if (((contrast==1) | (contrast==2)) & (kk==efloop))
      {
       conkey<-matrix(conkey[2:nrow(conkey),],nrow=(nrow(conkey)-1))
       if (outscreen==1)
       {cat("\nSpecific indirect effect contrast definition(s):\n")
       write.table(conkey,quote=FALSE,row.names=contlbs,col.names=FALSE,sep = "  ")}
      }
      if ((contrast==3) & (kk=efloop))
      {
       crowlbs<-rowlbs[2:(nindfx+1),1]
       contvect<-noquote(matrix(sprintf(decimals,contvec),nrow=1))
       colnames(contvect)<-crowlbs
       rownames(contvect)<-"(C1)"
       if (outscreen==1)
       {cat("\nSpecific indirect effect contrast weights:\n")
       print(contvect,right=T)}
      }
      if ((contrast==2) & (kk==efloop) & (outscreen==1))
      {cat("\nContrasts are differences between absolute values of indirect effects\n")}
     }
     if ((serial==1) & (kk==efloop))
     {rowlbst<-matrix(rowlbs[2:nrow(rowlbs),1],nrow<-(nrow(rowlbs)-1))
     if (outscreen==1)
     {cat("\nIndirect effect key:\n")
     write.table(indkey,quote=FALSE,row.names=rowbls3,col.names=FALSE,sep=" ")}}
    }
    if (nxvls > 1)
    {
     if (outscreen==1)
     { 
      if (kk==1){cat("\nRelative indirect effects of X on Y:\n")}
      if (kk==2){cat("\nPartially standardized relative indirect effects of X on Y:\n")}
      if (kk==3){cat("\nCompletely standardized relative indirect effects of X on Y:\n")}
     }
     for (i in (1:nrow(indmake)))
     {
      indtabsm<-indtab[(((i-1)*nxvls)+1):(nxvls*i),]
      indkeyt<-indkey[i,]
      if (outscreen==1)
      {
       cat("\n")
       write.table(t(indkeyt),quote=FALSE,row.names=FALSE,col.names=FALSE,sep=" ")
       cat("\n")
      }
      if (bcmat[nrow(bcmat),1]==0)
      {direffl2<-xcatlab[1:nxvls,1]}
      outformres<-process.outform3(indtabsm,maxresm,resultm)
      maxresm<-unlist(outformres[2]);resultm<-matrix(unlist(outformres[1]),ncol=maxresm)
      indtasm2<-noquote(matrix(sprintf(decimals,indtabsm),nrow=nrow(indtabsm)))
      colnames(indtasm2)<-bootlbs
      rownames(indtasm2)<-direffl2
      if (outscreen==1)
      {print(indtasm2,right=T)}
      if ((normal==1) & (sobelok==1) & (kk==1))
      {
       sobelsm=sobelmat[(((i-1)*nxvls)+1):(nxvls*i),]
       sobellab=c("Effect",hclab,"Z","p")
       outformres<-process.outform3(sobelsm,maxresm,resultm)
       maxresm<-unlist(outformres[2]);resultm<-matrix(unlist(outformres[1]),ncol=maxresm)
       sobelsm2<-noquote(matrix(sprintf(decimals,sobelsm),nrow=nrow(sobelsm)))
       colnames(sobelsm2)<-sobellab
       rownames(sobelsm2)<-direffl2
       if (outscreen==1)
       {cat("\n   Normal theory test for relative indirect effects:\n")
       print(sobelsm2,right=T)}
      }
     }
    }
    if ((effsize==1) & (boot > 0)){bootres=bootres[2:nrow(bootres),]}
   }
   if (alttotal==1)
   {
    altcnms<-"Effect"
    totbtvec<-totbtvec+bootdir
    alttotfx<-t(totbtvec[1,])
    if (boot > 0)
    {
     alttotfx<-matrix(0,ncol(totbtvec),4)
     alttotfx[,1]<-t(totbtvec[1,])
     for (cec in (1:ncol(totbtvec)))
     {
      if (bc==0){bcitmp<-process.pboot3(totbtvec[2:nrow(totbtvec),cec],cilow,cihigh)}
      if (bc==1)
      {
       bcbout<-process.bcboot3(totbtvec[2:nrow(totbtvec),cec],totbtvec[1,cec],xp2,badend,priorlo,priorhi)
       bcitmp<-matrix(unlist(bcbout[1]))
       badend<-unlist(bcbout[2]);priorlo<-unlist(bcbout[3]);priorhi<-unlist(bcbout[4])
      }
      alttotfx[cec,2:4]<-t(bcitmp)
     }
     altcnms<-c(altcnms,"BootSE","BootLLCI","BootULCI")
    }
     outformres<-process.outform3(alttotfx,maxresm,resultm)
     maxresm<-unlist(outformres[2]);resultm<-matrix(unlist(outformres[1]),ncol=maxresm)
     if (outscreen==1)
     {
     if (nxvls > 1)
      {cat("----------\n")
      cat("\nRelative total effects of X on Y (sum of direct and indirect effects):\n")
      alttotfp<-noquote(matrix(sprintf(decimals,alttotfx),nrow=nrow(alttotfx)))
      colnames(alttotfp)<-altcnms
      rownames(alttotfp)<-direffl2
      print(alttotfp,right=T)
      }
      if (nxvls==1)
      {
      cat("\nTotal effects of X on Y (sum of direct and indirect effects):\n")
      alttotfp<-noquote(matrix(sprintf(decimals,alttotfx),nrow=nrow(alttotfx)))
      colnames(alttotfp)<-altcnms
      rownames(alttotfp)<-" "
      print(alttotfp,right=T)
      }
     }
   }

  }
  #this is the end of the no moderators loop */

  if (anymod > 0)
  {
   if (boot==0){bootres<-obscoeff;indtab<-999}
   if (boot > 0){bootres<-rbind(obscoeff,bootres);indtab<-matrix(999,1,4)}
   if (sum(as.numeric(indmod > 0))==nrow(indmod))
   {if (outscreen==1)
    {
     if (nxvls > 1)
     {
      if (xmint==0)
      {cat("\nRelative conditional indirect effects of X on Y:\n")}
      if (xmint==1)
      {cat("\nRelative (total) natural indirect effects of X on Y:\n\n")}
     }
     if (nxvls==1)
     {
      if (xmint==0)
      {cat("\nConditional indirect effects of X on Y:\n")}
      if (xmint==1)
      {cat("\n (Total) Natural indirect effect(s) of X on Y:\n\n")}
     }
    }
   }
   if (sum(as.numeric(indmod > 0)) < nrow(indmod))
   {if (outscreen==1)
    {
     if (nxvls > 1)
     {cat("\nRelative conditional and unconditional indirect effects of X on Y:\n")}
     if (nxvls==1)
     {cat("\nConditional and unconditional indirect effects of X on Y:\n")}
    }
   }
   cftotfx=matrix(0,nrow(bootres),nxvls)
   for (i in (1:nrow(indmake)))
   {
    indtab<-matrix(0,1,4)
    indkeyt<-indkey[i,]
    if (outscreen==1)
    {
     if (xmint==0)
     {cat("\nINDIRECT EFFECT:\n\n")}
     write.table(t(indkeyt),quote=FALSE,row.names=FALSE,col.names=FALSE,sep=" ")
     cat("\n")
    }
    if (indmod[i,1]==0)
    {
     for (j in (1:nxvls))
     {
      indtemp<-matrix(1,nrow(bootres),1)
      for (k in (1:indmake[i,1]))
      {
       jtemp<-1
       if ((j > 1) & (k==1)){jtemp<-j}       
       indtemp<-indtemp*bootres[,pathsfoc[jtemp,indmake[i,(k+1)]]]
      }
      indeff<-indtemp[1,1]
      if (boot > 0)
      {
       if (bc==0){bcitmp3<-process.pboot3(indtemp[2:nrow(indtemp),1],cilow,cihigh)}
       if (bc==1)
       {
        bcbout<-process.bcboot3(indtemp[2:nrow(indtemp),1],indtemp[1,1],xp2,badend,priorlo,priorhi)
        bcitmp3<-matrix(unlist(bcbout[1]))
        badend<-unlist(bcbout[2]);priorlo<-unlist(bcbout[3]);priorhi<-unlist(bcbout[4])
       }
       indeff<-cbind(indeff,t(bcitmp3))
      }  
      indtab<-rbind(indtab,indeff)
     }
     indtab<-matrix(indtab[2:nrow(indtab),],ncol=ncol(indtab))
     outformres<-process.outform3(indtab,maxresm,resultm)
     maxresm<-unlist(outformres[2]);resultm<-matrix(unlist(outformres[1]),ncol=maxresm)
     indtab10<-noquote(matrix(sprintf(decimals,indtab),nrow=nrow(indtab)))
     if (nxvls > 1)
     {rownames(indtab10)<-xcatlab[1:nxvls,1]}
     if (nxvls==1)
     {rownames(indtab10)<-" "}
     colnames(indtab10)<-c("Effect","BootSE","BootLLCI","BootULCI")
     if (outscreen==1)
     {print(indtab10,right=T)}
    }
    # end of unmoderated
    # start of moderated
    if (indmod[i,1] > 0)
    {
     if (indmod[i,1]==1)
     {indmodva<-wmodvals;indprova<-wprobval;condlbs<-wnames;printw<-1}
     if (indmod[i,1]==2)
     {indmodva<-zmodvals;indprova<-zprobval;condlbs<-znames;printz<-1}
     if (indmod[i,1]==3)
     {
      cntmp<-1;printz<-1;printw<-1
      indmodva<-matrix(999,(nrow(wmodvals)*nrow(zmodvals)),2)
      for (k7 in (1:nrow(wmodvals)))
      {
       for (k8 in (1:nrow(zmodvals)))
       {indmodva[cntmp,]<-cbind(wmodvals[k7,1],zmodvals[k8,1]);cntmp<-cntmp+1}
      }
      condlbs<-cbind(wnames,znames)
     }
     condres<-matrix(999,nrow(indmodva),1)
     if (boot > 0)
     {condres=matrix(999,nrow(indmodva),4)}  
     condres<-cbind(indmodva,condres)

     # Here is where the computations start
     for (k4 in (1:nxvls))
     {
      imm3<-matrix(1,nrow(bootres),1);imm4<-matrix(1,nrow(bootres),1);indcontr<-0  
      if (indmod[i,1]==3){tihsw<-wprobval;tihsz<-zprobval}
      for (k1 in (1:nrow(indmodva)))
      {
       tucker2<-matrix(1,nrow(bootres),1)
       imm2<-matrix(1,nrow(bootres),1)
       wfirst<-0;zfirst<-0;immset<-0
       for (k2 in (1:indmake[i,1]))
       { 
        colnumb<-indmake[i,(k2+1)]
        if (k2==1)
        {
         wbb<-matrix(0,nrow(bootres),(nwvls*nxvls))
         zbb<-matrix(0,nrow(bootres),(nzvls*nxvls))
         wzbb<-matrix(0,nrow(bootres),(nwvls*nzvls*nxvls))
        }
        if (k2 != 1)
        {
         wbb<-matrix(0,nrow(bootres),nwvls)
         zbb<-matrix(0,nrow(bootres),nzvls)
         wzbb<-matrix(0,nrow(bootres),(nwvls*nzvls))
        }
        cnt<-1;tihs<-matrix(indlocs[2:((indlocs[1,colnumb])+1),colnumb])
        if (k2==1)
        {
         focbb<-matrix(tihs[1:nxvls,1]);focbb<-matrix(bootres[,focbb],ncol=nxvls)
         if (indmmm[i,1] > 0)
         {imm<-matrix(focbb[,k4]);condbb<-matrix(imm,ncol=ncol(imm))}
         focaddon<-matrix(0,1,nxvls);focaddon[1,k4]<-1;cnt<-cnt+nxvls;placeh<-nxvls
         if (indmod[i,1]==1)
         {
          tihsz<-matrix(0,nrow(wprobval),(nzvls*nxvls))
          tihswz<-matrix(0,nrow(wprobval),(nwvls*nzvls*nxvls))
          if (pathsw[1,colnumb]==1)
          {
           temp<-matrix(0,nrow(wprobval),(nxvls*nwvls))
           for (k5 in (1:nrow(wprobval)))
            {
             for (k6 in (1:nwvls))
             {temp[k5, (((k4-1)*nwvls)+k6)]<-wprobval[k5,k6]}
            }
            indprova<-cbind(temp,tihsz,tihswz)
          } else {
            indprova<-cbind(wprobval,tihsz,tihswz)}
         }
         if (indmod[i,1]==2)
         {
          tihsw<-matrix(0,nrow(zprobval),(nwvls*nxvls))
          tihswz<-matrix(0,nrow(zprobval),(nwvls*nzvls*nxvls))
          if (pathsz[1,colnumb]==1)
          {
           temp<-matrix(0,nrow(zprobval),(nxvls*nzvls))
           for (k5 in (1:nrow(zprobval)))
           {
            for (k6 in (1:nzvls))
            {temp[k5,(((k4-1)*nzvls)+k6)]<-zprobval[k5,k6]}  
           }
            indprova<-cbind(tihsw,temp,tihswz)
          } else {
            indprova<-cbind(tihsw,zprobval,tihswz)}
         }
         if (indmod[i,1]==3)
         {
          indprova<-matrix(0,(nrow(wprobval)*nrow(zprobval)),((ncol(wprobval)*nxvls)+(ncol(zprobval)*nxvls)+(nwvls*nzvls*nxvls)))
          cntemp<-1           
          for (k7 in (1:nrow(wprobval)))
          {
           for (k8 in (1:nrow(zprobval)))
           {
            temp<-(wprobval[k7,]*focaddon[1,k4])
            indprova[cntemp,(((k4-1)*nwvls)+1):(k4*(nwvls))]<-temp
            temp<-zprobval[k8,]*focaddon[1,k4]
            indprova[cntemp,((((k4-1)*nzvls)+1)+(nxvls*nwvls)):((((k4-1)*nzvls)+1)+(nxvls*nwvls)+(nzvls-1))]<-temp
            cntemp<-cntemp+1
           }
          }
          if (pathsz[1,colnumb]==0)
          {
           temp<-matrix(0,nrow(indprova),(ncol(zprobval)*nxvls))
           indprova[,((ncol(wprobval)*nxvls)+1):((ncol(wprobval)+ncol(zprobval))*nxvls)]<-temp
          }
          if (pathsw[1,colnumb]==0)
          {
           temp<-matrix(0,nrow(indprova),(ncol(wprobval)*nxvls))
           indprova[,1:(ncol(wprobval)*nxvls)]<-temp   
          }
          if (pathswz[1,colnumb]==1)
          {
           cntemp<-(ncol(wprobval)*nxvls)+(ncol(zprobval)*nxvls)+((k4-1)*ncol(wprobval)*ncol(zprobval))+1
           for (k7 in (1:ncol(wprobval)))
           {
            for (k8 in (1:ncol(zprobval)))
            {
             indprova[,cntemp]<-matrix(indprova[,((ncol(wprobval)*(k4-1))+k7)])*matrix(indprova[,((((k4-1)*ncol(zprobval))+k8)+(nxvls*ncol(wprobval)))])
             cntemp<-cntemp+1
            }
           }
          }
         }
        }
        if (k2 > 1)
        {
         focbb<-tihs[1,1];focbb=matrix(bootres[,focbb])
         if (indmmm[i,1] > 0){imm<-matrix(focbb[,1]);condbb<-matrix(imm)}     
         focaddon<-matrix(1);cnt<-cnt+1;placeh<-1
         if (indmod[i,1]==1)
         {
          tihsz<-matrix(0,nrow(wprobval),nzvls)
          tihswz<-matrix(0,nrow(wprobval),(nwvls*nzvls))
          indprova<-cbind(wprobval,tihsz,tihswz)
         }
         if (indmod[i,1]==2)
         {
          tihsw<-matrix(0,nrow(zprobval),nwvls)
          tihswz<-matrix(0,nrow(zprobval),(nwvls*nzvls))
          indprova<-cbind(tihsw,zprobval,tihswz)
         }
         if (indmod[i,1]==3)
         {
          indprova<-matrix(0,(nrow(wprobval)*nrow(zprobval)),((ncol(wprobval)+ncol(zprobval))+(nwvls*nzvls)))
          cntemp<-1           
          for (k7 in (1:nrow(wprobval)))
          {
           for (k8 in (1:nrow(zprobval)))
           {
            indprova[cntemp,1:(ncol(wprobval)+ncol(zprobval))]<-cbind(t(matrix(wprobval[k7,])),t(matrix(zprobval[k8,])))
            cntemp<-cntemp+1
           }
          }
          if (pathsz[1,colnumb]==0)
          {
           temp<-matrix(0,nrow(indprova),ncol(zprobval))
           indprova[,(ncol(wprobval)+1):(ncol(wprobval)+ncol(zprobval))]<-temp
          }
          if (pathsw[1,colnumb]==0)
          {
           temp<-matrix(0,nrow(indprova),ncol(wprobval))
           indprova[,1:ncol(wprobval)]<-temp   
          }
          if (pathswz[1,colnumb]==1)
          {
           cntemp<-(ncol(wprobval)+ncol(zprobval)+1)
           for (k7 in (1:ncol(wprobval)))
           {
            for (k8 in (1:ncol(zprobval)))
            {
             indprova[,cntemp]<-matrix(indprova[,k7])*matrix(indprova[,(ncol(wprobval)+k8)])
             cntemp<-cntemp+1
            }
           }
          }
         }
        }  
        if (pathsw[1,colnumb]==1)
        {
         wbb<-matrix(tihs[cnt:(cnt+(placeh*nwvls)-1),1])
         wbb<-matrix(bootres[,wbb],ncol=nrow(wbb))
         immlbs2<-matrix(wcatlab[1:nwvls,1])
         if (zfirst==0){wfirst<-1}              
         if ((indmmm[i,1]==1) | (indmmm[i,1]==31) | (indmmm[i,1]==51))
         {
          imm<-matrix(wbb[,1])
          for (k7 in (1:nwvls))
          {imm<-cbind(imm,wbb[,(((k4-1)*nwvls*(as.numeric(k2==1)))+k7)])}
           imm<-matrix(imm[,2:ncol(imm)],ncol=(ncol(imm)-1))               
         }
         if ((indmmm[i,1]==41) | (indmmm[i,1]==51))
         {
          condbb<-matrix(0,nrow(bootres),1)
          for (k7 in (1:nwvls))        
          {condbb<-cbind(condbb,wbb[,(((k4-1)*nwvls*(as.numeric(k2==1)))+k7)])}          
          condbb<-matrix(condbb[,2:ncol(condbb)],ncol=(ncol(condbb)-1))
         }    
         cnt<-cnt+(placeh*nwvls)
        }
        if (pathsz[1,colnumb]==1)
 	  {
         zbb<-matrix(tihs[cnt:(cnt+(placeh*nzvls)-1),1])
         zbb<-matrix(bootres[,zbb],ncol=nrow(zbb))
         if (wfirst==0){zfirst<-1}
         if (indmmm[i,1] != 31){immlbs2<-matrix(zcatlab[1:nzvls,1])}
         if ((indmmm[i,1]==2) | (indmmm[i,1]==31) | (indmmm[i,1]==51))
         {
          if (indmmm[i,1]==2){imm<-matrix(zbb[,1])}
          for (k7 in (1:nzvls))
          {imm<-cbind(imm,zbb[,(((k4-1)*nzvls*(as.numeric(k2==1)))+k7)])}
          if ((indmmm[i,1]==2) | (indmmm[i,1]==51))
          {
           imm<-matrix(imm[,2:ncol(imm)],ncol=(ncol(imm)-1))  
           if (indmmm[i,1]==51){condbb<-cbind(condbb,imm)}
          }         
         }
         cnt<-cnt+(placeh*nzvls)
        }
        if (pathswz[1,colnumb]==1)
        {
         wzbb<-matrix(tihs[cnt:(cnt+(placeh*nwvls*nzvls)-1),1])
         wzbb<-matrix(bootres[,wzbb],ncol=nrow(wzbb))
         if (indmmm[i,1]==41)
         {
          imm<-matrix(wzbb[,1])              
          for (k7 in (1:(nwvls*nzvls)))
          {imm<-cbind(imm,wzbb[,(((k4-1)*nzvls*nwvls*(as.numeric(k2==1)))+k7)])}
         }
         if (indmmm[i,1]==41)
         {
          imm<-matrix(imm[,2:ncol(imm)],ncol=(ncol(imm)-1))
          condbb<-cbind(condbb,imm[,(ncol(imm)-(nwvls*nzvls)+1):ncol(imm)]) 
         }
          cnt<-cnt+(placeh*nzvls*nwvls)
        }
        indprobe<-cbind(focaddon,t(matrix(indprova[k1,])))
        tucker<-cbind(focbb,wbb,zbb,wzbb)
        for (k3 in (1:ncol(indprobe)))
        {tucker[,k3]<-matrix(tucker[,k3]*indprobe[1,k3])}
        tucker2<-tucker2*matrix(rowSums(tucker))
        if ((indmmm[i,1]==1) | (indmmm[i,1]==2) | (indmmm[i,1]==31) | (indmmm[i,1]==41) | (indmmm[i,1]==51))
        {
         if (immset==1)
         {
          if ((ncol(imm2)==1) & (ncol(imm)==1)){imm2<-(matrix(imm2)*matrix(imm))}
          if ((indmmm[i,1]==41) | (indmmm[i,1]==51))
          {
           if ((ncol(condbb2) > 1) & (ncol(condbb) > 1))
           {
            condbb2t<-matrix(-999999,nrow(condbb2),(ncol(condbb2)*ncol(condbb)))
            k9<-1
            if (wfirst==1)
            {
             for (k7 in (1:ncol(condbb2)))
             {
              for (k8 in (1:ncol(condbb)))
              {condbb2t[,k9]<-(matrix(condbb2[,k7])*matrix(condbb[,k8]));k9<-k9+1}
             }
            }
            if (zfirst==1)
            {
             for (k7 in (1:ncol(condbb)))
             {
              for (k8 in (1:ncol(condbb2)))
              {condbb2t[,k9]<-(matrix(condbb[,k7])*matrix(condbb2[,k8]));k9<-k9+1}             
             }
            }
            condbb2<-matrix(condbb2t,ncol=ncol(condbb2t))            
           }
           if ((ncol(condbb2) > 1) & (ncol(condbb)==1))
           {
            for (k7 in (1:ncol(condbb2)))
            {condbb2[,k7]<-(matrix(condbb2[,k7])*matrix(condbb))}
           }
           if ((ncol(condbb2)==1) & (ncol(condbb) > 1))
           {
            for (k7 in (1:ncol(condbb)))
            {condbb[,k7]<-(matrix(condbb2)*matrix(condbb[,k7]))}
            condbb2<-matrix(condbb,ncol=ncol(condbb))
           }                 
          }
          if ((ncol(imm2) != 1) & (ncol(imm) != 1))
          {
           imm2t<-matrix(-999999,nrow(imm2),(ncol(imm2)*ncol(imm)))
           k9<-1
           if (wfirst==1)
           {
            for (k7 in (1:ncol(imm2)))
            {
             for (k8 in (1:ncol(imm)))
             {imm2t[,k9]<-(matrix(imm2[,k7])*matrix(imm[,k8]));k9<-k9+1}
            }
           }
           if (zfirst==1)
           {
            for (k7 in (1:ncol(imm)))
            {
             for (k8 in (1:ncol(imm2)))
             {imm2t[,k9]<-(matrix(imm[,k7])*matrix(imm2[,k8]));k9<-k9+1}
            }
           }
           imm2<-matrix(imm2t,ncol=ncol(imm2t))
          }
          if ((ncol(imm2) > 1) & (ncol(imm)==1))
          {
           for (k7 in (1:ncol(imm2)))
           {imm2[,k7]<-(matrix(imm2[,k7])*matrix(imm))}
          }
          if ((ncol(imm2)==1) & (ncol(imm) > 1))
          {
           for (k7 in (1:ncol(imm)))
           {imm[,k7]<-(matrix(imm2)*matrix(imm[,k7]))}
           imm2<-matrix(imm,ncol=ncol(imm))
          }
         }
         if (immset==0)
         {
          imm2<-matrix(imm,ncol=ncol(imm))
          if ((indmmm[i,1]==41) | (indmmm[i,1]==51))
          {condbb2<-matrix(condbb,ncol=ncol(condbb))}
          immset<-1
         }
        }
        # that is it for mmm loop */
       }
       # end of looping through paths: k2
       indtemp<-tucker2[1,1]
       if ((indmmm[i,1]==12) | (indmmm[i,1]==22))
       {
        imm3<-cbind(imm3,tucker2)
        if (k1==nrow(indmodva))
        {
         imm3<-matrix(imm3[,2:ncol(imm3)],ncol=(ncol(imm3)-1))
         immstop<-ncol(imm3)
         for (k8 in (2:immstop))
         {
          if (indmmmt[i,1]==1){imm3<-cbind(imm3,(imm3[,k8]-imm3[,1]))}
          if (indmmmt[i,1]==2){imm3=cbind(imm3,(imm3[,k8]-imm3[,(k8-1)]))}
          if (indmmmt[i,1]==3)
          {
           imm3<-cbind(imm3,((rowSums(imm3[,(k8:immstop)])/(immstop-k8+1))-imm3[,(k8-1)]))
          }
          if (indmmmt[i,1]==4)
          {imm3<-cbind(imm3,(imm3[,k8]-(rowSums(imm3[,1:immstop])/immstop)))}
         }
         if (indmmmt[i,1] < 5)
         {imm2<-matrix(imm3[,(immstop+1):ncol(imm3)],ncol=(ncol(imm3)-immstop))}
        }
       }      
       if ((indmmm[i,1]>-1) & ((contrast==1) | (contrast==2)))
       {
        imm4<-cbind(imm4,tucker2)
        if ((k1==nrow(indmodva)) & (k1 > 1))
        {
         imm4<-matrix(imm4[,2:ncol(imm4)],ncol=(ncol(imm4)-1))
         immstop<-ncol(imm4)
         condcont<-matrix(-999,(immstop*(immstop-1)/2),6)              
         for (k8 in (1:(immstop-1)))
         {
          for (k9 in ((k8+1):immstop))
          {                   
           if (contrast==1){imm4<-cbind(imm4,matrix(imm4[,k9]-imm4[,k8]))}
           if (contrast==2){imm4<-cbind(imm4,matrix(abs(imm4[,k9])-abs(imm4[,k8])))}
           condcont[(ncol(imm4)-immstop),1]<-imm4[1,k9]
           condcont[(ncol(imm4)-immstop),2]<-imm4[1,k8]
          }
         }
         imm4<-matrix(imm4[,(immstop+1):ncol(imm4)],ncol=(ncol(imm4)-immstop))   
         for (k8 in (1:ncol(imm4)))
         {
          condcont[k8,3]<-imm4[1,k8]
          if (boot > 0)
          {
           if (bc==0){condcon3<-process.pboot3(imm4[2:nrow(imm4),k8],cilow,cihigh)}
           if (bc==1)
           {
            bcbout<-process.bcboot3(imm4[2:nrow(imm4),k8],imm4[1,k8],xp2,badend,priorlo,priorhi)
            condcon3<-matrix(unlist(bcbout[1]))
            badend<-unlist(bcbout[2]);priorlo<-unlist(bcbout[3]);priorhi<-unlist(bcbout[4])
           }
           condcont[k8,4:6]<-t(condcon3)  
          }
         }
         if (boot==0){condcont<-matrix(condcont[,1:3],ncol=3)}
         indcontr<-1
        }
       }
       if (xmint==1)
       {
        if (k1==(k4+1))
        {cftotfx[,k4]<-cftotfx[,k4]+(tucker2*xscaling)}
       } 
       if (boot > 0)
       {
        tucker2<-tucker2*xscaling
        if (bc==0){bcitmp11<-process.pboot3(tucker2[2:nrow(tucker2),1],cilow,cihigh)}
        if (bc==1)
        {
         bcbout<-process.bcboot3(tucker2[2:nrow(tucker2),1],tucker2[1,1],xp2,badend,priorlo,priorhi)
         bcitmp11<-matrix(unlist(bcbout[1]))
         badend<-unlist(bcbout[2]);priorlo<-unlist(bcbout[3]);priorhi<-unlist(bcbout[4])
        }
        indtemp<-cbind((indtemp*xscaling),t(bcitmp11))  
       }
       if (boot==0)
       {indtemp<-indtemp*xscaling}
       condres[k1,(ncol(indmodva)+1):ncol(condres)]<-indtemp
      }
      # end of looping through indirect effects: k1
      # Here is where the computations end

      if (xmint==0)
      {
       outformres<-process.outform3(condres,maxresm,resultm)
       maxresm<-unlist(outformres[2]);resultm<-matrix(unlist(outformres[1]),ncol=maxresm)
      }
      condresp<-noquote(matrix(sprintf(decimals,condres),nrow=nrow(condres)))
      if (xmint==1)
      {
       if (k4==1)
       {
        natindfx<-matrix(condres[(2+(k4-1)),2:ncol(condres)],nrow=1)
       }
       if (k4 > 1)
       {
        natindfx<-rbind(natindfx,condres[(2+(k4-1)),2:ncol(condres)])
       }
       if (k4==nxvls)
       {
        outformres<-process.outform3(natindfx,maxresm,resultm)
        maxresm<-unlist(outformres[2]);resultm<-matrix(unlist(outformres[1]),ncol=maxresm)
       }
      }
      if (k4==1)
      {
       condlbs<-cbind(condlbs,"Effect")
       if (boot > 0)
       {condlbs<-cbind(condlbs,"BootSE","BootLLCI","BootULCI")}
       condrlb<-t(matrix(replicate(nrow(condres)," ")))
      }

      if (nxvls > 1)
      {condrlb<-matrix(xcatlab[k4,1],nrow(condres),1)}
      colnames(condresp)<-condlbs
      rownames(condresp)<-condrlb

      if ((outscreen==1) & (xmint==0))
      {print(condresp,right=T)}
      if ((outscreen==1) & (xmint==1) & (k4==nxvls))
      {
       natindf2<-noquote(matrix(sprintf(decimals,natindfx),nrow=nrow(natindfx)))
       colnames(natindf2)<-condlbs[,2:ncol(condlbs)]
       if (nxvls > 1)
        {rownames(natindf2)<-direffl2}
       if (nxvls==1)
        {rownames(natindf2)<-" "}
       print(natindf2,right=T)
       #here
       cat("\n")
      }


      if (indmmm[i,1]==0){cat("\n")}
      dichadj<-0;immcat<-0
      if ((indmmm[i,1] > 0) & (xmint==0))
      {
       if ((indmmm[i,1]==1) | (indmmm[i,1]==12) | (indmmm[i,1]==31))
       {
        if ((wdich==1) & (mcw==0))
        {
         if (indmmm[i,1] != 12){imm2[,1]<-imm2[,1]*(wmax-wmin)}
         if (indmmm[i,1] != 31){dichadj<-1}
        }           
        if (((mcw==1) | (mcw==2)) & (indmmm[i,1] != 31)){immcat<-1}
       }
       if ((indmmm[i,1]==2) | (indmmm[i,1]==22) | (indmmm[i,1]==31))
       {
        if ((zdich==1) & (mcz==0))
        {
         if (indmmm[i,1]==31)
         {
          imm2[,(nwvls+1):ncol(imm2)]<-matrix(imm2[,(nwvls+1):ncol(imm2)],ncol=(ncol(imm2)-nwvls))*(zmax-zmin)
         }
         if (indmmm[i,1]==2){imm2[,1]<-imm2[,1]*(zmax-zmin)}
         if (indmmm[i,1] != 31){dichadj<-1}
        }
        if (((mcz==1) | (mcz==2)) & (indmmm[i,1] != 31)){immcat<-1}
       }
       immtemp2<-t(matrix(imm2[1,],ncol=ncol(imm2)))
       immtemp<-matrix(immtemp2,ncol=ncol(immtemp2))
       immlbs<-"Index"
       if (boot > 0)
       {
        immtemp<-matrix(0,1,3)
        for (k7 in (1:ncol(imm2)))
        {
         if (bc==0){immbtci<-process.pboot3(imm2[2:nrow(imm2),k7],cilow,cihigh)} 
         if (bc==1)
         {
          bcbout<-process.bcboot3(imm2[2:nrow(imm2),k7],imm2[1,k7],xp2,badend,priorlo,priorhi)
          immbtci<-matrix(unlist(bcbout[1]))
          badend<-unlist(bcbout[2]);priorlo<-unlist(bcbout[3]);priorhi<-unlist(bcbout[4])
         }
         immtemp<-rbind(immtemp,t(immbtci))
        } 
        immtemp<-matrix(immtemp[2:nrow(immtemp),],ncol=ncol(immtemp))
        immtemp<-cbind(immtemp2,immtemp)
        immlbs<-c(immlbs,"BootSE","BootLLCI","BootULCI")
       }
       outformres<-process.outform3(immtemp,maxresm,resultm)
       maxresm<-unlist(outformres[2]);resultm<-matrix(unlist(outformres[1]),ncol=maxresm)
       immtempr<-noquote(matrix(sprintf(decimals,immtemp),nrow=nrow(immtemp)))
       colnames(immtempr)<-immlbs        
       if ((dichadj==0) & (immcat==0) & (indmmmt[i,1] != 5) & (indmmm[i,1] < 100))
       {
        if (indmmm[i,1] < 30)
        {
         rownames(immtempr)<-immlbs2
         if (outscreen==1)
         {cat("\n     Index of moderated mediation:\n")
         print(immtempr,right=T)}
         if ((nxvls > 1) & (k4 < nxvls)){cat("\n")}
        }
        if (indmmm[i,1]==31)
        {
         immlbs2<-rbind(immlbs2,matrix(zcatlab[1:nzvls,1]))
         rownames(immtempr)<-immlbs2
         if (outscreen==1)
         {cat("\n     Indices of partial moderated mediation:\n")
         print(immtempr,right=T)}
         if ((nxvls > 1) & (k4 < nxvls)){cat("\n")}
        }
        if ((nzvls==1) & (nwvls==1))
        {
         if ((indmmm[i,1]==41) | (indmmm[i,1]==51))
         {
          for (k7 in (1:nwvls))
          {
           immlbs2<-zcatlab[1:nzvls,1]
           immtemp2<-matrix(immtemp[(((k7-1)*nzvls)+1):(((k7-1)*nzvls)+nzvls),],ncol=ncol(immtemp))
           #resultm2<-matrix(99999,nrow(immtemp2),maxresm)
           #resultm2[1:nrow(immtemp2),1:ncol(immtemp2)]<-immtemp2
           #print(resultm)
           #print(resultm2)
           #resultm<-rbind(resultm,resultm2)
           #print(resultm)     
           immtem2r<-noquote(matrix(sprintf(decimals,immtemp2),nrow=nrow(immtemp2)))
           colnames(immtem2r)<-immlbs
           if (nwvls > 1)
           {
           primodv<-cbind("Primary moderator:", wcatlab[k7,1])
           if (outscreen==1)
           {write.table(primodv,quote=FALSE,row.names=FALSE,col.names=FALSE)}
           }
           if (nzvls==1)
           {
            rownames(immtem2r)<-" "
            if (outscreen==1)
            {cat("\n     Index of moderated moderated mediation:\n")
            print(immtem2r,right=T)}
           } else {
            rownames(immtem2r)<-immlbs2
            if (outscreen==1)            
            {cat("\n     Indices of moderated moderated mediation:\n")
            print(immtem2r,right=T)}
           }
           cmmtemp<-matrix(0,nrow(zprobval),4)        
           for (k8 in (1:nrow(zprobval)))
           {
            bbst<-((nwvls+1)+((k7-1)*nzvls));bben<-((nwvls+1)+((k7-1)*nzvls)+(nzvls-1))
            condbb3<-matrix(condbb2[,bbst:bben],ncol=(bben-bbst+1))
            if (ncol(zprobval) > 1)
            {
             condbb3<-condbb3*diag(c(zprobval[k8,]))
            } else {
             condbb3<-condbb3*zprobval[k8,]
            }
            condbb3<-cbind(condbb2[,k7],condbb3)
            icmm<-matrix(rowSums(condbb3))
            cmmtemp[k8,1]<-icmm[1,1]
            if (boot > 0)
            {
             if (bc==0){cmmt3<-process.pboot3(icmm[2:nrow(icmm),1],cilow,cihigh)}
             if (bc==1)
             {
              bcbout<-process.bcboot3(icmm[2:nrow(icmm),1],icmm[1,1],xp2,badend,priorlo,priorhi)
              cmmt3<-matrix(unlist(bcbout[1]))
              badend<-unlist(bcbout[2]);priorlo<-unlist(bcbout[3]);priorhi<-unlist(bcbout[4])
             }
             cmmtemp[k8,2:4]<-t(cmmt3)
            }
           cmmlbs<-cbind(znames,t(matrix(immlbs)))
           }
           if (boot==0){cmmtemp<-matrix(cmmtemp[,1:1],ncol=1);cmmlbs<-cbind(znames,"Index")}                   
           cmmtemp<-cbind(zmodvals,cmmtemp)
           outformres<-process.outform3(cmmtemp,maxresm,resultm)
           maxresm<-unlist(outformres[2]);resultm<-matrix(unlist(outformres[1]),ncol=maxresm)
           cmmtempr<-noquote(matrix(sprintf(decimals,cmmtemp),nrow=nrow(cmmtemp)))
           #if (boot==0){cmmtempr<-matrix(cmmtemp[,1:2],ncol=2)}                   
           #cmmlbs<-cbind(znames,t(matrix(immlbs)))
           rownames(cmmtempr)<-t(matrix(replicate(nrow(cmmtempr)," ")))
           colnames(cmmtempr)<-cmmlbs
           if (outscreen==1)
           {cat("\n     Indices of conditional moderated mediation by W:\n")
           print(cmmtempr,right=T)}
          }
         }
        }
       }
       if (((dichadj==1) | (immcat==1)) & (indmmm[i,1] < 30))
       {
        if (outscreen==1)
        {
         cat("\n     Index of moderated mediation\n")
         cat("     (differences beween conditional indirect effects):\n")
         rownames(immtempr)<-immlbs2
         print(immtempr,right=T)
        }
       }
      }
      if (indcontr==1)
      {
       outformres<-process.outform3(condcont,maxresm,resultm)
       maxresm<-unlist(outformres[2]);resultm<-matrix(unlist(outformres[1]),ncol=maxresm)
       condcnt3<-noquote(matrix(sprintf(decimals,condcont),nrow=nrow(condcont))) 
       condctlb<-c("Effect1","Effect2","Contrast","BootSE", "BootLLCI","BootULCI")
       colnames(condcnt3)<-condctlb
       rownames(condcnt3)<-t(matrix(replicate(nrow(condcnt3)," ")))
       if (outscreen==1)
       {
        cat("\n     Pairwise contrasts between conditional indirect effects\n")
        cat("     (Effect1 minus Effect2):\n")
        print(condcnt3,right=T)
       }
      }
      #if (outscreen==1)
      #{cat("\n---\n")}
     }
    }
    #end of moderated */
   }

   if (alttotal==1)
   {
    if (outscreen==1){cat("\n----------\n")}
    altcnms<-"Effect" 
    alttotfx<-matrix(cftotfx[1,])+obnatdfx
    if (boot > 0)
    {
     alttotfx<-matrix(0,ncol(cftotfx),4)
     alttotfx[,1]<-matrix(cftotfx[1,])+obnatdfx
     natdirbt[1,]<-t(obnatdfx)
     cftotfx<-cftotfx+natdirbt
     for (cec in (1:ncol(cftotfx)))
     {
      if (bc==0){cftot34<-process.pboot3(cftotfx[2:nrow(cftotfx),cec],cilow,cihigh)}
      if (bc==1)
      {
       bcbout<-process.bcboot3(cftotfx[2:nrow(cftotfx),cec],cftotfx[1,cec],xp2,badend,priorlo,priorhi)
       cftot34<-matrix(unlist(bcbout[1]))
       badend<-unlist(bcbout[2]);priorlo<-unlist(bcbout[3]);priorhi<-unlist(bcbout[4])
      }
      alttotfx[cec,2:4]<-t(cftot34)      
     }
     altcnms<-c(altcnms,"BootSE", "BootLLCI","BootULCI")
    }
    outformres<-process.outform3(alttotfx,maxresm,resultm)
    maxresm<-unlist(outformres[2]);resultm<-matrix(unlist(outformres[1]),ncol=maxresm)
    alttofx2<-noquote(matrix(sprintf(decimals,alttotfx),nrow=nrow(alttotfx)))
    if (outscreen==1)
    { 
     if (nxvls > 1)
     {
      cat("\nRelative total effects of X on Y (sum of direct and indirect effects:\n")
      rownames(alttofx2)<-direffl2
     }
     if (nxvls==1)
     {
      cat("\nTotal effect of X on Y (sum of direct and indirect effects:\n")
      rownames(alttofx2)<-" "
     }
     colnames(alttofx2)<-altcnms
     print(alttofx2,right=T)
    }
   }



  }
  # This is the end of the moderated loop */
 }


 if ((criterr==0) & (saveboot==1))
 {
  if (boot > 0)
  {
   if (outscreen==1)
   {
    cat("\n*********************************************************************** \n")
    conseq<-conseq[2:nrow(conseq),]
    #cat("Bootstrap estimates were saved in a global dataframe named process.boots\n")
    colslab<-cbind(savlabs,conseq,vlabs)
    cat("\nMap of column names to model coefficients in bootstrap matrix:\n")
    rownames(colslab)<-t(matrix(replicate(nrow(colslab)," ")))
    colnames(colslab)<-c("name","Conseqnt","Antecdnt")
    print(noquote(colslab),right=T)
   }
  }
 }

 #PRINT BOOTSTRAP RESULTS FOR MODEL PARAMETERS
 if ((criterr==0) & (boot > 0) & (modelbt==1))
 {
  labstart<-1
  if (outscreen==1)
  {cat("\n********** BOOTSTRAP RESULTS FOR REGRESSION MODEL PARAMETERS **********\n\n")}
  for (iboot in (1:(nms+nys)))
  {
   vlabsm<-matrix(vlabs[labstart:(labstart+(nump[1,iboot]-1)),1])
   bootcimt<-matrix(bootcim[labstart:(labstart+(nump[1,iboot]-1)),],ncol=5)
   outformres<-process.outform3(bootcimt,maxresm,resultm)
   maxresm<-unlist(outformres[2]);resultm<-matrix(unlist(outformres[1]),ncol=maxresm)
   bootcimt<-noquote(matrix(sprintf(decimals,bootcimt),ncol=5))
   colnames(bootcimt)<-matrix(c("Coeff","BootMean","BootSE","BootLLCI","BootULCI"))
   rownames(bootcimt)<-vlabsm
   if (outscreen==1)
   {
    cat("Outcome variable: ")
    write.table(outnames[iboot,1],quote=FALSE,row.names=FALSE,col.names=FALSE)
    cat("\n")
    print(bootcimt,right=T)
   }
   labstart<-labstart+nump[1,iboot]
   if ((iboot < (nms+nys)) & (outscreen==1)){cat("----------\n")}
  }
 }

 #print model matrices
 if ((criterr==0) & (matrices==1) & (outscreen==1))
 {
  cat("\n********************* MODEL DEFINITION MATRICES *********************** \n \n")
  cat("FROM variables are columns, TO variables are rows. \n \n")
  temp2<-matrix("0",nrow(bcmat),ncol(bcmat))
  for (i in c(2:nrow(bcmat)))
  {for (j in c(1:(ncol(bcmat)-1)))
   {if (bcmat[i,j]==1){temp2[i,j]<-"1"}
    if (j >= i){temp2[i,j]<-" "}}
  }
  temp2<-as.matrix(noquote(temp2[2:nrow(bcmat),(1:(ncol(bcmat)-1))]))
  if (nms > 0){cmatlabs<-c(xnames,mnames);rmatlabs<-c(mnames,ynames)}
  if (nms==0){cmatlabs<-xnames;rmatlabs<-ynames}
  colnames(temp2)<-cmatlabs
  rownames(temp2)<-rmatlabs
  cat("BMATRIX: Paths freely estimated (1) and fixed to zero (0): \n") 
  print(temp2)
  z<-0
  if (sum(wcmat) !=0)
  {
   temp2<-matrix("0",nrow(wcmat),ncol(wcmat))
   for (i in c(2:nrow(wcmat)))
   {for (j in c(1:(ncol(wcmat)-1)))
    {if (wcmat[i,j]==1){temp2[i,j]<-"1"}
     if (j >= i){temp2[i,j]<-" "}}
   }
   temp2<-as.matrix(noquote(temp2[2:nrow(wcmat),(1:(ncol(wcmat)-1))]))
   colnames(temp2)<-cmatlabs
   rownames(temp2)<-rmatlabs
   cat("\nWMATRIX: Paths moderated (1) and not moderated (0) by W: \n") 
   print(temp2)
  }
  if (sum(zcmat) !=0)
  {
   temp2<-matrix("0",nrow(zcmat),ncol(zcmat))
   for (i in c(2:nrow(zcmat)))
   {for (j in c(1:(ncol(zcmat)-1)))
    {if (zcmat[i,j]==1){temp2[i,j]<-"1"}
     if (j >= i){temp2[i,j]<-" "}}
   }
   temp2<-as.matrix(noquote(temp2[2:nrow(zcmat),(1:(ncol(zcmat)-1))]))
   colnames(temp2)<-cmatlabs
   rownames(temp2)<-rmatlabs
   cat("\nZMATRIX: Paths moderated (1) and not moderated (0) by Z: \n") 
   print(temp2)
  }
  if (sum(wzcmat) !=0)
  {
   temp2<-matrix("0",nrow(wzcmat),ncol(wzcmat))
   for (i in c(2:nrow(wzcmat)))
   {for (j in c(1:(ncol(wzcmat)-1)))
    {if (wzcmat[i,j]==1){temp2[i,j]<-"1"}
     if (j >= i){temp2[i,j]<-" "}}
   }
   temp2<-as.matrix(noquote(temp2[2:nrow(wzcmat),(1:(ncol(wzcmat)-1))]))
   colnames(temp2)<-cmatlabs
   rownames(temp2)<-rmatlabs
   cat("\nWZMATRIX: W moderated paths moderated (1) and not moderated (0) by Z: \n") 
   print(temp2)
  }
  if (ncs > 0)
  {colnames(ccmat)<-covnames
   rownames(ccmat)<-rmatlabs
   cat("\nCMATRIX: Covariates (columns) in (1) and not in (0) the models of M and Y (rows): \n")
   print(ccmat)
  }  
 }
 #end print model matrices

 if (criterr==0)
 {
  resultm<-resultm[2:nrow(resultm),]
  bocaj<-matrix(as.numeric(resultm==99999),nrow=nrow(resultm))
  bocaj<-(colSums(bocaj)==nrow(resultm))
  bocaj<-matrix(1-as.numeric(bocaj))
  j<-1
  for (i in (1:ncol(resultm)))
  {if (bocaj[i,1]==1){j<-j+1}}
  resultm<-resultm[,1:(j-1)]
  resultm[resultm==99999]<-NA
 }
 if ((outscreen==1) & (activate==0))
 {cat("\n******************** ANALYSIS NOTES AND ERRORS ************************ \n")}
 if (activate==1)
 {cat("*********************************************************************** \n")}
 if ((criterr==0) & (outscreen==1))
 {
  cat("\nLevel of confidence for all confidence intervals in output: ")
  write.table(conf,quote=FALSE,row.names=FALSE,col.names=FALSE)
  if (boot>0)
  { 
   if ((goodboot==boot) & (bc==0))
   {
    cat("\nNumber of bootstraps for percentile bootstrap confidence intervals: ")
    write.table(boot,quote=FALSE,row.names=FALSE,col.names=FALSE)  
   }
   if ((goodboot==boot) & (bc==1))
   {
    cat("\nNumber of bootstraps for bias-corrected bootstrap confidence intervals: ")
    write.table(boot,quote=FALSE,row.names=FALSE,col.names=FALSE)  
   }
   if (length(badend) > 1)
   {
    cat("\nWARNING: Bootstrap CI endpoints below are not trustworthy. Decrease confidence\n")
    cat("or increase the number of bootstrap samples.\n")
    badend<-matrix(badend);badend<-t(badend[2:nrow(badend),1])
    badend<-noquote(matrix(sprintf(decimals,badend),nrow=nrow(badend)))
    write.table(badend,quote=FALSE,row.names=FALSE,col.names=FALSE)
   }
  }
  if (mc > 0)
  {
   cat("\nNumber of samples for Monte Carlo confidence intervals: ")
   write.table(mc,quote=FALSE,row.names=FALSE,col.names=FALSE)  
  }
  if ((wnotev > 0) & (printw==1))
  {
   if (wnotev==1)
   {cat("\nW values in conditional tables are the 16th, 50th, and 84th percentiles.\n")}
   if ((wnotev==2) & (minwwarn==0) & (maxwwarn==0))
   {cat("\nW values in conditional tables are the mean and +/- SD from the mean.\n")}  
   if (minwwarn==1)
   {cat("\nW values in conditional tables are the minimum, the mean, and 1 SD above the mean.\n")} 
   if (maxwwarn==1)
   {cat("\nW values in conditional tables are 1 SD below the mean, the mean, and the maximum.\n")}
  }
  if ((znotev > 0) & (printz==1))
  {
   if (znotev==1)
   {cat("\nZ values in conditional tables are the 16th, 50th, and 84th percentiles.\n")}
   if ((znotev==2) & (minzwarn==0) & (maxzwarn==0))
   {cat("\nZ values in conditional tables are the mean and +/- SD from the mean.\n")}  
   if (minzwarn==1)
   {cat("\nZ values in conditional tables are the minimum, the mean, and 1 SD above the mean.\n")} 
   if (maxzwarn==1)
   {cat("\nZ values in conditional tables are 1 SD below the mean, the mean, and the maximum.\n")}
  }
  if (minwwarn > 0)
  {cat("\nNOTE: One SD below the mean is below the minimum observed in the data for W,\n")
   cat("      so the minimum measurement on W is used for conditioning instead.\n")}
  if (maxwwarn > 0)
  {cat("\nNOTE: One SD above the mean is above the maximum observed in the data for W,\n")
   cat("      so the maximum measurement on W is used for conditioning instead.\n")}
  if (minzwarn > 0)
  {cat("\nNOTE: One SD below the mean is below the minimum observed in the data for Z,\n")
   cat("      so the minimum measurement on Z is used for conditioning instead.\n")}
  if (maxzwarn > 0)
  {cat("\nNOTE: One SD above the mean is above the maximum observed in the data for Z,\n")
   cat("      so the maximum measurement on Z is used for conditioning instead.\n")}
  if (pstog==1)
  {cat("\nNOTE: Standardized coefficients for dichotomous or multicategorical X are\n")
   cat("      in partially standardized form.\n")}
  for (i in c(1:100)) 
  {
  if (notecode[i,1]==32)
  {
   cat("\n")
   cat("Direct, indirect, and total effects are counterfactually defined \n")
   if (xcontcf==0)
   {
    cat("assuming X by M interaction.\n")
   }
   if (xcontcf==1)
   {
    cat("assuming X by M interaction and with the following reference (x_ref) \n")
    cat("and counterfactual (x_cf) states for X: \n")
    xrefvals=t(xrefvals)
    xrefvals<-noquote(matrix(sprintf(decimals,xrefvals),nrow=nrow(xrefvals)))
    rownames(xrefvals)<-c("x_ref :","x_cf  :")
    colnames(xrefvals)<-" "
    print(xrefvals,right=T)
   }
  }
  if (notecode[i,1]==1)
   {cat(" \n")
   cat("NOTE: COVMY is ignored when using CMATRIX option. \n")}
  if (notecode[i,1]==2)
   {cat(" \n")
   cat("NOTE: Confidence level restricted to between 50 and 99.9999%. 95% confidence is provided in output. \n")}
  if (notecode[i,1]==3)
   {cat(" \n")
   cat("NOTE: The following variables were mean centered prior to analysis: \n")
   centvar<-c("      ", centvar)
   centvar<-t(noquote(centvar))  
   write.table(centvar,quote=FALSE,row.names=FALSE,col.names=FALSE)
   }
  if ((notecode[i,1]==4) & (ydich != 1))
   {cat(" \n")
   cat("NOTE: A heteroscedasticity consistent standard error and covariance matrix estimator was used. \n")}  
  if (notecode[i,1]==6)
   {cat(" \n")
   cat("NOTE: Due to estimation problems, some bootstrap samples had to be replaced. \n")
   cat("      The number of times this happened was: ")
   write.table(badboot,quote=FALSE,row.names=FALSE,col.names=FALSE)}  
  if (notecode[i,1]==7)
   {cat(" \n")
   cat("NOTE: The bootstrapping was not completed due to problematic bootstrap samples. \n")
   cat("      Bootstrap confidence intervals are therefore suppressed.\n")}
  if (notecode[i,1]==8)
   {cat(" \n")
   cat("NOTE: The number of bootstrap samples was adjusted upward given your desired confidence. \n")}  
  if (notecode[i,1]==9)
   {cat(" \n")
   cat("NOTE: WMODVAL is ignored when W is specified as multicategorical. \n")}  
  if (notecode[i,1]==10)
   {cat(" \n")
   cat("NOTE: ZMODVAL is ignored when Z is specified as multicategorical. \n")}
  if (notecode[i,1]==11)
   {cat(" \n")
   cat("NOTE: Total effect model generated only when all covariates are specified\n")
   cat("      in all models of M and Y.\n")}
  if (notecode[i,1]==12)
   {cat(" \n")
   cat("NOTE: Total effect model generated only when X is freely estimated to\n")
   cat("      affect each M and both X and M are freely estimated to affect Y.\n")}
  if (notecode[i,1]==30)
   {cat(" \n")
   cat("NOTE: Your vector of linear hypothesis weights is of the wrong length for this model. \n")}  
  if (notecode[i,1]==13)
   {cat(" \n")
   cat("NOTE: There are too many pairwise contrasts to conduct with this model. \n")}  
  if (notecode[i,1]==14)
   {cat(" \n")
   cat("NOTE: The number of contrast weights must equal the number of indirect effects. \n")}  
  if (notecode[i,1]==15)
   {cat(" \n")
   cat("NOTE: Monte Carlo confidence intervals not available for this model. \n")
   cat("      Bootstrapping is used instead.\n")}
  if (notecode[i,1]==16)
   {cat(" \n")
   cat("NOTE: The number of Monte Carlo samples was adjusted upward given your desired confidence. \n")}  
  if (notecode[i,1]==19)
   {cat(" \n")
   cat("NOTE: Your contrast matrix is invalid or not applicable to this model. \n")}  
  if (notecode[i,1]==20)
   {cat(" \n")
   cat("NOTE: One of the groups specified by your contrast matrix does not exist in the data. \n")}  
  if (notecode[i,1]==24)
   {cat(" \n")
   cat("NOTE: Total effect model not available with dichotomous Y. \n")}
  if (notecode[i,1]==25)
   {cat(" \n")
   cat("NOTE: STAND/EFFSIZE options not available with dichotomous Y. \n")}
  if (notecode[i,1]==27)
   {cat(" \n")
   cat("NOTE: Standardized coefficients not available for models with moderators. \n")}
  if (notecode[i,1]==28)
   {cat(" \n")
   cat("NOTE: The contrast option is not available with a multicategorical X. \n")}
  if (notecode[i,1]==31)
   {
    medmean2<-noquote(matrix(sprintf(decimals,medmeans),nrow=nrow(medmeans)))
    rownames(medmean2)<-" "
    colnames(medmean2)<-mnames
    if (nms > 1)
    {
    cat(" \n")
    cat("NOTE: Controlled direct effect(s) estimated at the following mediator values: \n")
    }
    if (nms==1)
    {
    cat(" \n")
    cat("NOTE: Controlled direct effect(s) estimated at the following mediator value: \n")
    }
    print(medmean2,right=T)
   }
  if (notecode[i,1]==33)
   {cat(" \n")
   cat("NOTE: Sobel test is not available when using the XMINT option. \n")}
  if (notecode[i,1]==34)
   {cat(" \n")
   cat("NOTE: Standardized effects are not available when using the XMINT option. \n")}
  if (notecode[i,1]==36)
   {cat(" \n")
   cat("NOTE: The XREFVAL option is ignored when X is declared as multicategorical. \n")}
  if (notecode[i,1]==37)
   {cat(" \n")
   cat("NOTE: The CONTRAST option is not available when using the XMINT option. \n")}
  if (notecode[i,1]==38)
   {cat(" \n")
   cat("NOTE: One or more cases were deleted prior to analysis at your request. \n")}
  if (notecode[i,1]==35)
   {cat(" \n")
   cat("NOTE: Counterfactual effects estimated at the following covariate values: \n")
   coval2<-noquote(matrix(sprintf(decimals,coval),nrow=nrow(coval)))
   rownames(coval2)<-" "
   colnames(coval2)<-covnames
   print(coval2,right=T)}
  if ((notecode[i,1]==29) & (listmiss==1))
   {cat(" \n")
   a<-missrow;conum<-ncol(missrow);allgood<-0;smremain<-12;largesti<-1;smallrow<-0;
   if (conum > 12)
      {
       for (ii in c(1:12))
       { 
        check<-(conum/ii);
        if (check==trunc(check))
        {
         check2<-conum/ii;aok<-ii
         if (aok > 2){allgood<-1}
        }
        if (check != trunc(check))
        {
         remain<-conum-(ii*trunc(check))
         if (remain <= smremain)
         {smremain<-remain;largesti<-ii;smallrow<-trunc(conum/largesti)}
        }
       }
       atemp<-a[1,1:(aok*check2)]
       atemp<-matrix(atemp,nrow=check2,byrow=TRUE)
       cat("NOTE: Missing data resulted in the deletion of the following row(s) of: \n")
       cat("      data from the analysis: \n")
       if (ncol(atemp) > 2)
       {prmatrix(atemp, rowlab=rep("    ",nrow(atemp)),collab=rep("    ",ncol(atemp)))}
       if (allgood==0)
       {
        atemp<-a[1,1:(smallrow*largesti)]
        atemp<-matrix(atemp,nrow=smallrow,byrow=TRUE)
        btemp<-matrix(a[1,((largesti*smallrow)+1):conum])
        prmatrix(atemp,rowlab=rep("    ",nrow(atemp)),collab=rep("    ",ncol(atemp)))
        prmatrix(btemp,rowlab=rep("    ",1),collab=rep("    ",ncol(btemp)))
       }
      }
      if (conum <= 12)
      {
       cat("NOTE: Missing data resulted in the deletion of the following row(s) of \n")
       cat("      data from the analysis: \n")
       prmatrix(a, rowlab=rep("    ",nrow(a)), collab=rep("    ",ncol(a)))
      }

   }
  }
  if ((nmiss > 0) & (listmiss==0))
  {cat(" \n")
   cat("NOTE: Some cases with missing data were deleted. The number of deleted cases was: ")
   write.table(nmiss,quote=FALSE,row.names=FALSE,col.names=FALSE)}
 }

 # print errors
 for (i in c(1:100)) 
 {
 if (errcode[i,1]==1)
  {cat(" \n")
  cat("ERROR: You must specify a Y and an X variable. \n")}  
 if (errcode[i,1]==2)
  {cat(" \n")
  cat("ERROR: X, M, or Y variable used more than once or W and Z are the same variable. \n")}  
 if (errcode[i,1]==3)
  {cat(" \n")
  cat("ERROR: You have specified more than one variable for W, Y, X, or Z. \n")} 
 if (errcode[i,1]==4)
  {cat(" \n")
  cat("ERROR: A variable specified as multicategorical has more than nine categories. \n")} 
 if (errcode[i,1]==5)
  {cat(" \n")
  cat("ERROR: One of the categories contains only a single case. \n")} 
 if (errcode[i,1]==6)
  {cat(" \n")
  cat("ERROR: Invalid model number in this version of PROCESS. \n")}  
 if (errcode[i,1]==7)
  {cat(" \n")
  cat("ERROR: Invalid model number. \n")}  
 if (errcode[i,1]==8)
  {cat(" \n")
  cat("ERROR: You must specify an M variable for this model. \n")} 
 if (errcode[i,1]==9)
  {cat(" \n")
  cat("ERROR: You have specified an M variable in a model that does not use it. \n")}
 if (errcode[i,1]==10)
  {cat(" \n")
  cat("ERROR: You have specified a W variable in a model that does not use it. \n")}
 if (errcode[i,1]==11)
  {cat(" \n")
  cat("ERROR: You have not specified a W variable in a model that requires it. \n")}
 if (errcode[i,1]==12)
  {cat(" \n")
  cat("ERROR: You have specified a Z variable in a model that does not use it. \n")}
 if (errcode[i,1]==13)
  {cat(" \n")
  cat("ERROR: You have not specified a Z variable in a model that requires it. \n")}
 if (errcode[i,1]==15)
  {cat(" \n")
  cat("ERROR: One of your model variables exhibits no variation (it is a constant). \n")}
 if (errcode[i,1]==16)
  {cat(" \n")
  cat("ERROR: BMATRIX is not the correct length or is otherwise invalid. \n")}
 if (errcode[i,1]==17)
  {cat(" \n")
  cat("ERROR: WMATRIX is not the correct length or is otherwise invalid. \n")}
 if (errcode[i,1]==18)
  {cat(" \n")
  cat("ERROR: ZMATRIX is not the correct length or is otherwise invalid. \n")}
 if (errcode[i,1]==19)
  {cat(" \n")
  cat("ERROR: WZMATRIX is not the correct length or is otherwise invalid. \n")}
 if (errcode[i,1]==20)
  {cat(" \n")
  cat("ERROR: A path fixed at zero cannot be moderated. \n")}
 if (errcode[i,1]==60)
  {cat(" \n")
  cat("PROCESS is now ready for use.\n")
  cat("Copyright 2020-2023 by Andrew F. Hayes ALL RIGHTS RESERVED\n")
  cat("Workshop schedule at http://haskayne.ucalgary.ca/CCRAM\n \n")}
 if (errcode[i,1]==21)
  {cat(" \n")
  cat("ERROR: If only one moderator is specified, it must be specified as W. \n")}
 if (errcode[i,1]==22)
  {cat(" \n")
  cat("ERROR: In BMATRIX, X must be specified to affect at least one variable. \n")}
 if (errcode[i,1]==23)
  {cat(" \n")
  cat("ERROR: In BMATRIX, at least one variable must be specified to affect Y. \n")}
 if (errcode[i,1]==24)
  {cat(" \n")
  cat("ERROR: You must specify a model number or a custom BMATRIX specification. \n")}  
 if (errcode[i,1]==25)
  {cat(" \n")
  cat("ERROR: BMATRIX cannot be used in conjunction with a model number. \n")}
 if (errcode[i,1]==26)
  {cat(" \n")
  cat("ERROR: Your model has a dangling mediator (all Ms must affect and be affected). \n")}
 if (errcode[i,1]==29)
  {cat(" \n")
  cat("ERROR: CMATRIX is not the correct length or is otherwise invalid. \n")}  
 if (errcode[i,1]==30)
  {cat(" \n")
  cat("ERROR: In CMATRIX, all covariates must be assigned to an M or a Y. \n")}
 if ((errcode[i,1]==31) & (singlr==0))
  {cat(" \n")
  singlr<-1
  cat("ERROR: A linear or near linear dependence (singularity) exists in the data. \n")}
 if (errcode[i,1]==32)
  {cat(" \n")
  cat("ERROR: Models 80 and 81 require between 3 and 6 mediators. \n")}  
 if (errcode[i,1]==33)
  {cat(" \n")
  cat("ERROR: Model 82 requires 4 mediators. \n")} 
 if (errcode[i,1]==34)
  {cat(" \n")
  cat("ERROR: This model number requires between 2 and 6 mediators. \n")} 
 if (errcode[i,1]==35)
  {cat(" \n")
  cat("ERROR: In a model with only one moderator, that moderator must be W. \n")}
 if (errcode[i,1]==36)
  {cat(" \n")
  cat("ERROR: A serial mediation model cannot have more than 6 mediators. \n")}  
 if (errcode[i,1]==37)
  {cat(" \n")
  cat("ERROR: No more than 10 mediators are allowed in a PROCESS command. \n")} 
 if (errcode[i,1]==38)
  {cat(" \n")
  cat("ERROR: XCATCODE is not provided, not the correct length, or is otherwise invalid. \n")} 
 if (errcode[i,1]==39)
  {cat(" \n")
  cat("ERROR: WCATCODE is not provided, not the correct length, or is otherwise invalid. \n")} 
 if (errcode[i,1]==40)
  {cat(" \n")
  cat("ERROR: ZCATCODE is not provided, not the correct length, or is otherwise invalid. \n")} 
 if (errcode[i,1]==41)
  {cat(" \n")
  cat("ERROR: Models 1, 2, and 3 cannot be customized. \n")}
 if (errcode[i,1]==43)
  {cat(" \n")
  cat("ERROR: PROCESS does not allow dichotomous mediators. \n")}
 if (errcode[i,1]==50)
  {cat(" \n")
  cat("ERROR: A multicategorical moderator cannot be specified as a covariate. \n")} 
 if (errcode[i,1]==51)
  {cat(" \n")
  cat("ERROR: A variable you specified as a covariate is a moderator in all equations. \n")} 
 if (errcode[i,1]==62)
  {cat(" \n")
  cat("ERROR: After listwise deletion of cases with missing data, too few cases remain. \n")} 
 if (errcode[i,1]==63)
  {cat(" \n")
  cat("ERROR: The XMINT option is available only for model 4. \n")}
 if (errcode[i,1]==64)
  {cat(" \n")
  cat("ERROR: Incorrect number of values specified in CDEVAL option. \n")}
 if (errcode[i,1]==65)
  {cat(" \n")
  cat("ERROR: Only indicator or sequential coding of X is allowed with the XMINT option. \n")}
 if (errcode[i,1]==66)
  {cat(" \n")
  cat("ERROR: A reference value of X is required for this model. \n")}
 if (errcode[i,1]==67)
  {cat(" \n")
  cat("ERROR: Too many elements provided in XREFVAL option. \n")}
 if (errcode[i,1]==68)
  {cat(" \n")
  cat("ERROR: Covariate assignment is not allowed with the XMINT option. \n")}
 if (errcode[i,1]==69)
  {cat(" \n")
  cat("ERROR: Incorrect number of values specified in COVAL option. \n")}
 if (errcode[i,1]==70)
  {cat(" \n")
  cat("ERROR: Incorrect value(s) in XREFVAL for this dichotomous X variable. \n")}
 if (errcode[i,1]==71)
  {cat(" \n")
  cat("ERROR: The CENTER option is not available when using the XMINT option. \n")}
 if (errcode[i,1]==72)
  {cat(" \n")
  cat("ERROR: The XMINT option is not available for models with a dichotomous Y. \n")}
 if ((errcode[i,1]==52) & (mcerpt==0))
  {mcerpt<-1
  cat(" \n")
  cat("ERROR: A variable specified as multicategorical must have at least three categories. \n")}
 if (errcode[i,1]==53)
  {cat(" \n")
  cat("ERROR: Variables declared as factors or that are non-numeric are not accepted by PROCESS.\n")}  
 }
 #if (saveboot==1)
 #{resultm<-list(resultm,boots)}
 #invisible(resultm)
 resultms<-NULL
 if ((saveboot==0) & (saveest==1)){resultms<-resultm}
 if ((saveboot==1) & (saveest==0)){resultms<-boots}
 if ((saveboot==1) & (saveest==1)){resultms<-(list(boots,resultm))}
 invisible(resultms)
}  
process(activate=1)



```


## descriptive statistics
```{r warning = FALSE, message = FALSE}

table(df$treatment)
# age
mean(df$age)
sd(df$age)
tapply(df$age, df$treatment, mean)
tapply(df$age, df$treatment, sd)
#standardize
# income
mean(df$INCOME)
sd(df$INCOME)
tapply(df$INCOME, df$treatment, mean)
tapply(df$INCOME, df$treatment, sd)
# education
mean(df$EDU)
sd(df$EDU)
tapply(df$EDU, df$treatment, mean)
tapply(df$EDU, df$treatment, sd)
# gender
table(df$GENDER)
table(df$GENDER, df$treatment)
# race
table(df$race)
table(df$race, df$treatment)

# outcome variable
# NID
mean(df$NID)
sd(df$NID)
tapply(df$NID, df$treatment, mean)
tapply(df$NID, df$treatment, sd)
# CTB
mean(df$CTB)
sd(df$CTB)
tapply(df$CTB, df$treatment, mean)
tapply(df$CTB, df$treatment, sd)

# Perceived threat
mean(df$PER_THREAT_1)
sd(df$PER_THREAT_1)
tapply(df$PER_THREAT_1, df$treatment, mean)
tapply(df$PER_THREAT_1, df$treatment, sd)

# Perceived threat
mean(df$PER_THREAT_1)
sd(df$PER_THREAT_1)
tapply(df$PER_THREAT_1, df$treatment, mean)
tapply(df$PER_THREAT_1, df$treatment, sd)

# feeling (sentiment)
mean(df$feeling_thermo)
sd(df$feeling_thermo)
tapply(df$feeling_thermo, df$treatment, mean)
tapply(df$feeling_thermo, df$treatment, sd)

# conspiracy
mean(df$conspiracy_belief)
sd(df$conspiracy_belief)
tapply(df$conspiracy_belief, df$treatment, mean)
tapply(df$conspiracy_belief, df$treatment, sd)

df <- df %>%
  mutate(across(c("age","GENDER","race","EDU","INCOME","CTB","feeling_thermo","conspiracy_belief","trueneg_belief","truepos_belief"), ~ scale(.x, center = TRUE, scale = TRUE)))

#m1 <- lm(feeling_thermo ~ real_threat + real_dethreat + symb_threat + symb_dethreat +
#           age + GENDER + race + EDU + INCOME + CTB,df)
#summary(m1)

#m1_2 <- lm(feeling_thermo ~ factor(treatment) +
#           age + GENDER + race + EDU + INCOME + CTB,df)
#summary(m1_2)

```



## 1.  conspiracy belief as dependent variable

### (1) realistic threat


```{r warning = FALSE, message = FALSE}
process(data = df, y = "conspiracy_belief", x = "real_threat", 
        m = "feeling_thermo", 
        cov = c("real_dethreat", "symb_threat", "symb_dethreat", "age","GENDER","race","EDU","INCOME","CTB","NID"), 
        model = 4,
        boot = 5000,
        seed = 123456,
        total = 1)

```

### (2) realistic dethreat

```{r warning = FALSE, message = FALSE}
process(data = df, y = "conspiracy_belief", x = "real_dethreat", 
        m = "feeling_thermo", 
        cov = c("real_threat", "symb_threat", "symb_dethreat", "age","GENDER","race","EDU","INCOME","CTB","NID"),
        model = 4,
        boot = 5000,
        seed = 123456,
        total = 1)

```

### (3) symbolic threat

```{r warning = FALSE, message = FALSE}
process(data = df, y = "conspiracy_belief", x = "symb_threat", 
        m = "feeling_thermo", 
        cov = c("real_threat", "real_dethreat", "symb_dethreat", "age","GENDER","race","EDU","INCOME","CTB","NID"), 
        model = 4,
        boot = 5000,
        seed = 123456,
        total = 1)

```

### (4) symbolic dethreat

```{r warning = FALSE, message = FALSE}


process(data = df, y = "conspiracy_belief", x = "symb_dethreat", 
        m = "feeling_thermo",
        cov = c("real_threat", "real_dethreat", "symb_threat","age","GENDER", "race", "EDU","INCOME","CTB","NID"), 
        model = 4,
        boot = 5000,
        seed = 123456,
        total = 1)

```

## 2.  true negative news as dependent variable

### (1) realistic threat

```{r warning = FALSE, message = FALSE}
process(data = df, y = "trueneg_belief", x = "real_threat", 
        m = "feeling_thermo", 
        cov = c("real_dethreat", "symb_threat", "symb_dethreat", "age","GENDER","race","EDU","INCOME","CTB","NID"), 
        model = 4,
        boot = 5000,
        seed = 123456,
        total = 1)

```

### (2) realistic dethreat

```{r warning = FALSE, message = FALSE}
process(data = df, y = "trueneg_belief", x = "real_dethreat", 
        m = "feeling_thermo", 
        cov = c("real_threat", "symb_threat", "symb_dethreat", "age","GENDER","race","EDU","INCOME","CTB","NID"),
        model = 4,
        boot = 5000,
        seed = 123456,
        total = 1)

```

### (3) symbolic threat

```{r warning = FALSE, message = FALSE}
process(data = df, y = "trueneg_belief", x = "symb_threat", 
        m = "feeling_thermo", 
        cov = c("real_threat", "real_dethreat", "symb_dethreat", "age","GENDER","race","EDU","INCOME","CTB","NID"), 
        model = 4,
        boot = 5000,
        seed = 123456,
        total = 1)

```

### (4) symbolic dethreat

```{r warning = FALSE, message = FALSE}


process(data = df, y = "trueneg_belief", x = "symb_dethreat", 
        m = "feeling_thermo",
        cov = c("real_threat", "real_dethreat", "symb_threat","age","GENDER", "race", "EDU","INCOME","CTB","NID"), 
        model = 4,
        boot = 5000,
        seed = 123456,
        total = 1)

```

## 3.  true positive news as dependent variable

### (1) realistic threat

```{r warning = FALSE, message = FALSE}
process(data = df, y = "truepos_belief", x = "real_threat", 
        m = "feeling_thermo", 
        cov = c("real_dethreat", "symb_threat", "symb_dethreat", "age","GENDER","race","EDU","INCOME","CTB","NID"), 
        model = 4,
        boot = 5000,
        seed = 123456,
        total = 1)

```

### (2) realistic dethreat

```{r warning = FALSE, message = FALSE}
process(data = df, y = "truepos_belief", x = "real_dethreat", 
        m = "feeling_thermo", 
        cov = c("real_threat", "symb_threat", "symb_dethreat", "age","GENDER","race","EDU","INCOME","CTB","NID"),
        model = 4,
        boot = 5000,
        seed = 123456,
        total = 1)

```

### (3) symbolic threat

```{r warning = FALSE, message = FALSE}
process(data = df, y = "truepos_belief", x = "symb_threat", 
        m = "feeling_thermo", 
        cov = c("real_threat", "real_dethreat", "symb_dethreat", "age","GENDER","race","EDU","INCOME","CTB","NID"), 
        model = 4,
        boot = 5000,
        seed = 123456,
        total = 1)

```

### (4) symbolic dethreat

```{r warning = FALSE, message = FALSE}

process(data = df, y = "truepos_belief", x = "symb_dethreat", 
        m = "feeling_thermo",
        cov = c("real_threat", "real_dethreat", "symb_threat","age","GENDER", "race", "EDU","INCOME","CTB","NID"), 
        model = 4,
        boot = 5000,
        seed = 123456,
        total = 1)

```


## 4. realistic vs. symbolic

### (1) comparing realistic de-threat with symbolic de-threat - no effects

```{r warning = FALSE, message = FALSE}

# recode treatment - realistic threat as reference

df$control1 <- 0
df$control1[df$treatment == 1] <- 1
df$real_threat1 <- 0
df$real_threat1[df$treatment == 2] <- 1
df$symb_threat1 <- 0
df$symb_threat1[df$treatment == 4] <- 1
df$symb_dethreat1 <- 0
df$symb_dethreat1[df$treatment == 5] <- 1

process(data = df, y = "conspiracy_belief", x = "symb_dethreat1", 
        m = "feeling_thermo", 
        cov = c("control1", "real_threat1", "symb_threat1", "age","GENDER","race","EDU","INCOME","CTB"), 
        model = 4,
        boot = 5000,
        seed = 123456,
        total = 1
        )

```


### (2) comparing realistic threat with symbolic threat - no effects

```{r warning = FALSE, message = FALSE}

# recode treatment - realistic threat as reference

df$control2 <- 0
df$control2[df$treatment == 1] <- 1
df$real_dethreat2 <- 0
df$real_dethreat2[df$treatment == 2] <- 1
df$symb_threat2 <- 0
df$symb_threat2[df$treatment == 4] <- 1
df$symb_dethreat2 <- 0
df$symb_dethreat2[df$treatment == 5] <- 1

process(data = df, y = "conspiracy_belief", x = "symb_threat2", 
        m = "feeling_thermo", 
        cov = c("control2", "real_dethreat2", "symb_dethreat2", "age","GENDER","race","EDU","INCOME","CTB"), 
        model = 4,
        boot = 5000,
        seed = 123456,
        total = 1)

```

## manipulation check

```{r warning = FALSE, message = FALSE}
df$treatment <- as.factor(df$treatment)
df <- within(df, treatment <- relevel(treatment, ref = "1"))
m <- lm(PER_THREAT_1 ~ treatment, data = df)
options(scipen=999)

summary(m)
confint(m,level = 0.95)


mean(df$truepos_belief[df$treatment == 1])
mean(df$truepos_belief[df$treatment == 2])
mean(df$truepos_belief[df$treatment == 3])
mean(df$truepos_belief[df$treatment == 4])
mean(df$truepos_belief[df$treatment == 5])
m1 <- lm(truepos_belief ~ factor(treatment) + age + GENDER +
           race + EDU + INCOME + CTB, df)
summary(m1)
```
